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Remote Sensing: 15th Anniversary

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 15 February 2025 | Viewed by 36073

Special Issue Editor


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Guest Editor
Senior Scientist (ST), U. S. Geological Survey (USGS), USGS Western Geographic Science Center (WGSC), 2255, N. Gemini Dr., Flagstaff, AZ 86001, USA
Interests: hyperspectral remote sensing, remote sensing expertise in a number of areas including: (a) global croplands, (b) agriculture, (c) water resources, (d) wetlands, (e) droughts, (f) land use/land cover, (g) forestry, (h) natural resources management, (i) environments, (j) vegetation, and (k) characterization of large river basins and deltas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As 2024 will mark the 15th anniversary of the Remote Sensing (ISSN 2072-4292) journal, this milestone is an opportune moment for us to take pride in our many achievements over the past 15 years.

Remote sensing and geospatial science are indispensable for monitoring and analyzing surface elements at various scales, both at community and global levels. In particular, new knowledge mining and scientific discovery through remote sensing is especially critical in the era of increasing proliferation of massive remote sensing data and spatio-temporal big data. 

This Special Issue collates the latest research results and progress in the field of remote sensing, including new technologies, breakthroughs in this area, and its wide-ranging applications in forests, oceans, agriculture, the atmosphere, geology, etc. Both original research papers and comprehensive literature reviews with unique scientific insights are welcome.

Dr. Prasad S. Thenkabail
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (32 papers)

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Research

22 pages, 4798 KiB  
Article
Advancing Algorithmic Adaptability in Hyperspectral Anomaly Detection with Stacking-Based Ensemble Learning
by Bradley J. Wheeler and Hassan A. Karimi
Remote Sens. 2024, 16(21), 3994; https://doi.org/10.3390/rs16213994 - 28 Oct 2024
Viewed by 300
Abstract
Anomaly detection in hyperspectral imaging is crucial for remote sensing, driving the development of numerous algorithms. However, systematic studies reveal a dichotomy where algorithms generally excel at either detecting anomalies in specific datasets or generalizing across heterogeneous datasets (i.e., lack adaptability). A key [...] Read more.
Anomaly detection in hyperspectral imaging is crucial for remote sensing, driving the development of numerous algorithms. However, systematic studies reveal a dichotomy where algorithms generally excel at either detecting anomalies in specific datasets or generalizing across heterogeneous datasets (i.e., lack adaptability). A key source of this dichotomy may center on the singular and like biases frequently employed by existing algorithms. Current research lacks experimentation into how integrating insights from diverse biases might counteract problems in singularly biased approaches. Addressing this gap, we propose stacking-based ensemble learning for hyperspectral anomaly detection (SELHAD). SELHAD introduces the integration of hyperspectral anomaly detection algorithms with diverse biases (e.g., Gaussian, density, partition) into a singular ensemble learning model and learns the factor to which each bias should contribute so anomaly detection performance is optimized. Additionally, it introduces bootstrapping strategies into hyperspectral anomaly detection algorithms to further increase robustness. We focused on five representative algorithms embodying common biases in hyperspectral anomaly detection and demonstrated how they result in the previously highlighted dichotomy. Subsequently, we demonstrated how SELHAD learns the interplay between these biases, enabling their collaborative utilization. In doing so, SELHAD transcends the limitations inherent in individual biases, thereby alleviating the dichotomy and advancing toward more adaptable solutions. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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32 pages, 9991 KiB  
Article
Exploring Topological Information Beyond Persistent Homology to Detect Geospatial Objects
by Meirman Syzdykbayev and Hassan A. Karimi
Remote Sens. 2024, 16(21), 3989; https://doi.org/10.3390/rs16213989 - 27 Oct 2024
Viewed by 577
Abstract
Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topological knowledge-based (Topological KB) method that leverages the [...] Read more.
Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topological knowledge-based (Topological KB) method that leverages the integration of topological, geometrical, and contextual information to enhance the precision of landslide detection. Topology, a fundamental branch of mathematics, explores the properties of space that are preserved under continuous transformations and focuses on the qualitative aspects of space, studying features like connectivity and exitance of loops/holes. We employed persistent homology (PH) to derive candidate polygons and applied three distinct strategies for landslide detection: without any filters, with geometrical and contextual filters, and a combination of topological with geometrical and contextual filters. Our method was rigorously tested across five different study areas. The experimental results revealed that geometrical and contextual filters significantly improved detection accuracy, with the highest F1 scores achieved when employing these filters on candidate polygons derived from PH. Contrary to our initial hypothesis, the addition of topological information to the detection process did not yield a notable increase in accuracy, suggesting that the initial topological features extracted through PH suffices for accurate landslide characterization. This study advances the field of geospatial object detection by demonstrating the effectiveness of combining geometrical and contextual information and provides a robust framework for accurately mapping landslide susceptibility. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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24 pages, 18639 KiB  
Article
National-Scale Detection of New Forest Roads in Sentinel-2 Time Series
by Øivind Due Trier and Arnt-Børre Salberg
Remote Sens. 2024, 16(21), 3972; https://doi.org/10.3390/rs16213972 - 25 Oct 2024
Viewed by 307
Abstract
The Norwegian Environment Agency is responsible for updating a map of undisturbed nature, which is performed every five years based on aerial photos. Some of the aerial photos are already up to five years old when a new version of the map of [...] Read more.
The Norwegian Environment Agency is responsible for updating a map of undisturbed nature, which is performed every five years based on aerial photos. Some of the aerial photos are already up to five years old when a new version of the map of undisturbed nature is published. Thus, several new nature interventions may have been missed. To address this issue, the timeliness and mapping accuracy were improved by integrating Sentinel-2 satellite imagery for the detection of new roads across Norway. The focus on new roads was due to the fact that most new nature interventions include the construction of new roads. The proposed methodology is based on applying U-Net on all the available summer images with less than 10% cloud cover over a five-year period, with an aggregation step to summarize the predictions. The observed detection rate was 98%. Post-processing steps reduced the false positive rate to 46%. However, as the false positive rate was still substantial, the manual verification of the predicted new roads was needed. The false negative rate was low, except in areas without vegetation. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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25 pages, 26385 KiB  
Article
An Innovative Tool for Monitoring Mangrove Forest Dynamics in Cuba Using Remote Sensing and WebGIS Technologies: SIGMEM
by Alexey Valero-Jorge, Raúl González-Lozano, Roberto González-De Zayas, Felipe Matos-Pupo, Rogert Sorí and Milica Stojanovic
Remote Sens. 2024, 16(20), 3802; https://doi.org/10.3390/rs16203802 - 12 Oct 2024
Viewed by 604
Abstract
The main objective of this work was to develop a viewer with web output, through which the changes experienced by the mangroves of the Gran Humedal del Norte de Ciego de Avila (GHNCA) can be evaluated from remote sensors, contributing to the understanding [...] Read more.
The main objective of this work was to develop a viewer with web output, through which the changes experienced by the mangroves of the Gran Humedal del Norte de Ciego de Avila (GHNCA) can be evaluated from remote sensors, contributing to the understanding of the spatiotemporal variability of their vegetative dynamics. The achievement of this objective is supported by the use of open-source technologies such as MapStore, GeoServer and Django, as well as Google Earth Engine, which combine to offer a robust and technologically independent solution to the problem. In this context, it was decided to adopt an action model aimed at automating the workflow steps related to data preprocessing, downloading, and publishing. A visualizer with web output (Geospatial System for Monitoring Mangrove Ecosystems or SIGMEM) is developed for the first time, evaluating changes in an area of central Cuba from different vegetation indices. The evaluation of the machine learning classifiers Random Forest and Naive Bayes for the automated mapping of mangroves highlighted the ability of Random Forest to discriminate between areas occupied by mangroves and other coverages with an Overall Accuracy (OA) of 94.11%, surpassing the 89.85% of Naive Bayes. The estimated net change based on the year 2020 of the areas determined during the classification process showed a decrease of 5138.17 ha in the year 2023 and 2831.76 ha in the year 2022. This tool will be fundamental for researchers, decision makers, and students, contributing to new research proposals and sustainable management of mangroves in Cuba and the Caribbean. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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20 pages, 3755 KiB  
Article
Multidirectional Attention Fusion Network for SAR Change Detection
by Lingling Li, Qiong Liu, Guojin Cao, Licheng Jiao, Fang Liu, Xu Liu and Puhua Chen
Remote Sens. 2024, 16(19), 3590; https://doi.org/10.3390/rs16193590 - 26 Sep 2024
Viewed by 527
Abstract
Synthetic Aperture Radar (SAR) imaging is essential for monitoring geomorphic changes, urban transformations, and natural disasters. However, the inherent complexities of SAR, particularly pronounced speckle noise, often lead to numerous false detections. To address these challenges, we propose the Multidirectional Attention Fusion Network [...] Read more.
Synthetic Aperture Radar (SAR) imaging is essential for monitoring geomorphic changes, urban transformations, and natural disasters. However, the inherent complexities of SAR, particularly pronounced speckle noise, often lead to numerous false detections. To address these challenges, we propose the Multidirectional Attention Fusion Network (MDAF-Net), an advanced framework that significantly enhances image quality and detection accuracy. Firstly, we introduce the Multidirectional Filter (MF), which employs side-window filtering techniques and eight directional filters. This approach supports multidirectional image processing, effectively suppressing speckle noise and precisely preserving edge details. By utilizing deep neural network components, such as average pooling, the MF dynamically adapts to different noise patterns and textures, thereby enhancing image clarity and contrast. Building on this innovation, MDAF-Net integrates multidirectional feature learning with a multiscale self-attention mechanism. This design utilizes local edge information for robust noise suppression and combines global and local contextual data, enhancing the model’s contextual understanding and adaptability across various scenarios. Rigorous testing on six SAR datasets demonstrated that MDAF-Net achieves superior detection accuracy compared with other methods. On average, the Kappa coefficient improved by approximately 1.14%, substantially reducing errors and enhancing change detection precision. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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29 pages, 33649 KiB  
Article
Comparison of Time-Lapse Ground-Penetrating Radar and Electrical Resistivity Tomography Surveys for Detecting Pig (Sus spp.) Cadaver Graves in an Australian Environment
by Victoria Berezowski, Xanthé Mallett, Dilan Seckiner, Isabella Crebert, Justin Ellis, Gabriel C. Rau and Ian Moffat
Remote Sens. 2024, 16(18), 3498; https://doi.org/10.3390/rs16183498 - 20 Sep 2024
Viewed by 1564
Abstract
Locating clandestine graves presents significant challenges to law enforcement agencies, necessitating the testing of grave detection techniques. This experimental study, conducted under Australian field conditions, assesses the effectiveness of time-lapse ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) in detecting pig burials as [...] Read more.
Locating clandestine graves presents significant challenges to law enforcement agencies, necessitating the testing of grave detection techniques. This experimental study, conducted under Australian field conditions, assesses the effectiveness of time-lapse ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) in detecting pig burials as simulated forensic cases. The research addresses two key questions: (1) observability of graves using GPR and ERT, and (2) changes in geophysical responses with reference to changing climatic conditions. The principal novelty of this research is its Australian focus—this is the first time-lapse GPR and ERT study used to locate clandestine graves in Australia. The results reveal that both GPR and ERT can detect graves; however, ERT demonstrates greater suitability in homogeneous soil and anomalously wet climate conditions, with the detectability affected by grave depth. This project also found that resistivity values are likely influenced by soil moisture and decomposition fluids; however, these parameters were not directly measured in this study. Contrastingly, although GPR successfully achieved 2 m penetration in each survey, the site’s undeveloped soil likely resulted in inconsistent detectability. The findings underscore the significance of site-specific factors when employing GPR and/or ERT for grave detection, including soil homogeneity, climate conditions, water percolation, and body decomposition state. These findings offer practical insights into each technique’s utility as a search tool for missing persons, aiding law enforcement agencies with homicide cases involving covert graves. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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23 pages, 5621 KiB  
Article
Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods
by Shashank Karki, Thomas J. Pingel, Timothy D. Baird, Addison Flack and Todd Ogle
Remote Sens. 2024, 16(18), 3453; https://doi.org/10.3390/rs16183453 - 18 Sep 2024
Viewed by 1186
Abstract
Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. [...] Read more.
Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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23 pages, 1814 KiB  
Article
Doppler-Spread Space Target Detection Based on Overlapping Group Shrinkage and Order Statistics
by Linsheng Bu, Tuo Fu, Defeng Chen, Huawei Cao, Shuo Zhang and Jialiang Han
Remote Sens. 2024, 16(18), 3413; https://doi.org/10.3390/rs16183413 - 13 Sep 2024
Viewed by 754
Abstract
The Doppler-spread problem is commonly encountered in space target observation scenarios using ground-based radar when prolonged coherent integration techniques are utilized. Even when the translational motion is accurately compensated, the phase resulting from changes in the target observation attitude (TOA) still leads to [...] Read more.
The Doppler-spread problem is commonly encountered in space target observation scenarios using ground-based radar when prolonged coherent integration techniques are utilized. Even when the translational motion is accurately compensated, the phase resulting from changes in the target observation attitude (TOA) still leads to extension of the target’s echo energy across multiple Doppler cells. In particular, as the TOA change undergoes multiple cycles within a coherent processing interval (CPI), the Doppler spectrum spreads into equidistant sparse line spectra, posing a substantial challenge for target detection. Aiming to address such problems, we propose a generalized likelihood ratio test based on overlapping group shrinkage denoising and order statistics (OGSos-GLRT) in this study. First, the Doppler domain signal is denoised according to its equidistant sparse characteristics, allowing for the recovery of Doppler cells where line spectra may be situated. Then, several of the largest Doppler cells are integrated into the GLRT for detection. An analytical expression for the false alarm probability of the proposed detector is also derived. Additionally, a modified OGSos-GLRT method is proposed to make decisions based on an increasing estimated number of line spectra (ENLS), thus increasing the robustness of OGSos-GLRT when the ENLS mismatches the actual value. Finally, Monte Carlo simulations confirm the effectiveness of the proposed detector, even at low signal-to-noise ratios (SNRs). Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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21 pages, 39606 KiB  
Article
Mapping Building Heights at Large Scales Using Sentinel-1 Radar Imagery and Nighttime Light Data
by Mohammad Kakooei and Yasser Baleghi
Remote Sens. 2024, 16(18), 3371; https://doi.org/10.3390/rs16183371 - 11 Sep 2024
Viewed by 747
Abstract
Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building [...] Read more.
Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1’s signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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25 pages, 9394 KiB  
Article
Microscale Temperature-Humidity Index (THI) Distribution Estimated at the City Scale: A Case Study in Maebashi City, Gunma Prefecture, Japan
by Kotaro Iizuka, Yuki Akiyama, Minaho Takase, Toshikazu Fukuba and Osamu Yachida
Remote Sens. 2024, 16(17), 3164; https://doi.org/10.3390/rs16173164 - 27 Aug 2024
Viewed by 1050
Abstract
Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island [...] Read more.
Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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22 pages, 5669 KiB  
Article
Multi-Stage Feature Fusion of Multispectral and SAR Satellite Images for Seasonal Crop-Type Mapping at Regional Scale Using an Adapted 3D U-Net Model
by Lucas Wittstruck, Thomas Jarmer and Björn Waske
Remote Sens. 2024, 16(17), 3115; https://doi.org/10.3390/rs16173115 - 23 Aug 2024
Cited by 1 | Viewed by 692
Abstract
Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still [...] Read more.
Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still represent a major challenge in the field of remote sensing. In this paper, we propose a novel three-dimensional (3D) deep learning U-Net model to fuse multi-level image features from multispectral and synthetic aperture radar (SAR) time series data for seasonal crop-type mapping at a regional scale. For this purpose, we used a dual-stream U-Net with a 3D squeeze-and-excitation fusion module applied at multiple stages in the network to progressively extract and combine multispectral and SAR image features. Additionally, we introduced a distinctive method for generating patch-based multitemporal multispectral composites by selective image sampling within a 14-day window, prioritizing those with minimal cloud cover. The classification results showed that the proposed network provided the best overall accuracy (94.5%) compared to conventional two-dimensional (2D) and three-dimensional U-Net models (2D: 92.6% and 3D: 94.2%). Our network successfully learned multi-modal dependencies between the multispectral and SAR satellite images, leading to improved field mapping of spectrally similar and heterogeneous classes while mitigating the limitations imposed by persistent cloud coverage. Additionally, the feature representations extracted by the proposed network demonstrated their transferability to a new cropping season, providing a reliable mapping of spatio-temporal crop type patterns. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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17 pages, 10747 KiB  
Article
Evaluation and Improvement of a CALIPSO-Based Algorithm for Cloud Base Height in China
by Ruolin Li and Xiaoyan Ma
Remote Sens. 2024, 16(15), 2801; https://doi.org/10.3390/rs16152801 - 31 Jul 2024
Viewed by 895
Abstract
Clouds are crucial in regulating the Earth’s energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was [...] Read more.
Clouds are crucial in regulating the Earth’s energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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26 pages, 3922 KiB  
Article
LSTT: Long-Term Spatial–Temporal Tensor Model for Infrared Small Target Detection under Dynamic Background
by Deyong Lu, Wei An, Qiang Ling, Dong Cao, Haibo Wang, Miao Li and Zaiping Lin
Remote Sens. 2024, 16(15), 2746; https://doi.org/10.3390/rs16152746 - 27 Jul 2024
Viewed by 677
Abstract
Infrared small target detection is an important and core problem in infrared search and track systems. Many infrared small target detection methods work well under the premise of a static background; however, the detection effect decreases seriously when the background changes dynamically. In [...] Read more.
Infrared small target detection is an important and core problem in infrared search and track systems. Many infrared small target detection methods work well under the premise of a static background; however, the detection effect decreases seriously when the background changes dynamically. In addition, the spatiotemporal information of the target and background of the image sequence are not fully developed and utilized, lacking long-term temporal characteristics. To solve these problems, a novel long-term spatial–temporal tensor (LSTT) model is proposed in this paper. The image registration technique is employed to realize the matching between frames. By directly superimposing the aligned images, the spatiotemporal features of the resulting tensor are not damaged or reduced. From the perspective of the horizontal slice of this tensor, it is found that the background component has similarity in the time dimension and correlation in the space dimension, which is more consistent with the prerequisite of low rank, while the target component is sparse. Therefore, we transform the problem of infrared detection of a small moving target into a low-rank sparse decomposition problem of new tensors composed of several continuous horizontal slices of the aligned image tensor. The low rank of the background is constrained by the partial tubal nuclear norm (PTNN), and the tensor decomposition problem is quickly solved using the alternating-direction method of multipliers (ADMM). Our experimental results demonstrate that the proposed LSTT method can effectively detect small moving targets against a dynamic background. Compared with other benchmark methods, the new method has better performance in terms of detection efficiency and accuracy. In particular, the new LSTT method can extract the spatiotemporal information of more frames in a longer time domain and obtain a higher detection rate. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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21 pages, 12559 KiB  
Article
Improving Error Estimates for Evaluating Satellite-Based Atmospheric CO2 Measurement Concepts through Numerical Simulations
by Bruna Barbosa Silveira, Vincent Cassé, Olivier Chomette and Cyril Crevoisier
Remote Sens. 2024, 16(13), 2452; https://doi.org/10.3390/rs16132452 - 3 Jul 2024
Viewed by 932
Abstract
To assess the accuracy of satellite monitoring of anthropogenic CO2 emissions, inversions of satellite data in SWIR are usually combined with the assimilation of the total CO2 column into a Kalman filter that reconstructs the sources and sinks of atmospheric [...] Read more.
To assess the accuracy of satellite monitoring of anthropogenic CO2 emissions, inversions of satellite data in SWIR are usually combined with the assimilation of the total CO2 column into a Kalman filter that reconstructs the sources and sinks of atmospheric CO2. To provide error estimates of the total CO2 column for multi-month assimilation experiments of simulated satellite data, we parametrise these errors using linear regressions. These regression are obtained from a database that links meteorological situations, albedos, and aerosols to the errors in the inversion of the total CO2 column based on simulated satellite data for those conditions. The errors in this database are explicitly computed using the Bayesian estimation formalism, and the linear regressions are optimised by selecting appropriate predictors and predictants. For different levels of measurement noise, error simulations are performed over a period of several months using the albedo and aerosol data from MODIS. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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20 pages, 10516 KiB  
Article
Improving the Spatiotemporal Resolution of Land Surface Temperature Using a Data Fusion Method in Haihe Basin, China
by Rencai Lin, Zheng Wei, He Chen, Congying Han, Baozhong Zhang and Maomao Jule
Remote Sens. 2024, 16(13), 2374; https://doi.org/10.3390/rs16132374 - 28 Jun 2024
Viewed by 772
Abstract
Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation [...] Read more.
Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation challenges. Moreover, the existing LST products are constrained by their low spatiotemporal resolution, limiting their broader applicability. The fusion of multi-source remote sensing data offers a viable solution to enhance spatiotemporal resolution. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used to estimate time series LST utilizing multi-temporal Landsat 8 (L8) and MOD21A2 within the Haihe basin in 2021. Validation of ESTARFM LST was conducted against L8 LST and in situ LST. The results can be summarized as follows: (1) ESTARFM was found to be effective in heterogeneous regions within the Haihe basin, yielding LST with a spatiotemporal resolution of 30 m and 8 d while retaining clear texture information; (2) the comparison between ESTARFM LST and L8 LST shows a coefficient determination (R2) exceeding 0.59, a mean absolute error (MAE) lower than 2.43 K, and a root mean square error (RMSE) lower than 2.63 K for most dates; (3) comparison between ESTARFM LST and in situ LST showcased high validation accuracy, revealing a R2 of 0.87, a MAE of 2.27 K, and a RMSE of 4.12 K. The estimated time series LST exhibited notable reliability and robustness. This study introduced ESTARFM for LST estimation, achieving satisfactory outcomes. The findings offer a valuable reference for other regions to generate LST data with a spatiotemporal resolution of 8 d and 30 m, thereby enhancing the application of data products in agriculture and hydrology contexts. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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17 pages, 1824 KiB  
Article
Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery
by Kai Cui, Changlong Wang, Feng Zhou, Chunheng Liu, Yongchan Gao and Weike Feng
Remote Sens. 2024, 16(13), 2294; https://doi.org/10.3390/rs16132294 - 23 Jun 2024
Cited by 1 | Viewed by 656
Abstract
In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to [...] Read more.
In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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22 pages, 49542 KiB  
Article
A Robust Target Detection Algorithm Based on the Fusion of Frequency-Modulated Continuous Wave Radar and a Monocular Camera
by Yanqiu Yang, Xianpeng Wang, Xiaoqin Wu, Xiang Lan, Ting Su and Yuehao Guo
Remote Sens. 2024, 16(12), 2225; https://doi.org/10.3390/rs16122225 - 19 Jun 2024
Cited by 3 | Viewed by 740
Abstract
Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed [...] Read more.
Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed to address these issues in this paper. Firstly, a lane detection algorithm is used to process the image to obtain lane information. Then, two-dimensional fast Fourier transform (2D-FFT), constant false alarm rate (CFAR), and density-based spatial clustering of applications with noise (DBSCAN) are used to process the radar data. Furthermore, the YOLOv5 algorithm is used to process the image. In addition, the lane lines are utilized to filter out the interference targets from outside lanes. Finally, multi-sensor information fusion is performed for targets in the same lane. Experiments show that the balanced score of the proposed algorithm can reach 0.98, which indicates that it has low false and missed detections. Additionally, the balanced score is almost unchanged in different environments, proving that the algorithm is robust. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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27 pages, 5983 KiB  
Article
Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning
by Peng Fu, Christian Clanton, Kirk M. Demuth, Verena Goodman, Lauren Griffith, Mage Khim-Young, Julia Maddalena, Kenny LaMarca, Logan A. Wright, David W. Schurman and James R. Kellner
Remote Sens. 2024, 16(12), 2217; https://doi.org/10.3390/rs16122217 - 19 Jun 2024
Cited by 1 | Viewed by 2569
Abstract
Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of [...] Read more.
Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS). Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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18 pages, 12154 KiB  
Article
Blind Edge-Retention Indicator for Assessing the Quality of Filtered (Pol)SAR Images Based on a Ratio Gradient Operator and Confidence Interval Estimation
by Xiaoshuang Ma, Le Li and Gang Wang
Remote Sens. 2024, 16(11), 1992; https://doi.org/10.3390/rs16111992 - 31 May 2024
Viewed by 531
Abstract
Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well [...] Read more.
Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well after processing. Some quantitative assessment indicators have been presented to evaluate the edge-preservation capability of single-polarization SAR filters, among which the non-clean-reference-based (i.e., blind) ones are attractive. However, most of these indicators are derived based only on the basic fact that the speckle is a kind of multiplicative noise, and they do not take into account the detailed statistical distribution traits of SAR data, making the assessment not robust enough. Moreover, to our knowledge, there are no specific blind assessment indicators for fully Polarimetric SAR (PolSAR) filters up to now. In this paper, a blind assessment indicator based on an SAR Ratio Gradient Operator (RGO) and Confidence Interval Estimation (CIE) is proposed. The RGO is employed to quantify the edge gradient between two neighboring image patches in both the speckled and filtered data. A decision is then made as to whether the ratio gradient value in the filtered image is close to that in the unobserved clean image by considering the statistical traits of speckle and a CIE method. The proposed indicator is also extended to assess the PolSAR filters by transforming the polarimetric scattering matrix into a scalar which follows a Gamma distribution. Experiments on the simulated SAR dataset and three real-world SAR images acquired by ALOS-PALSAR, AirSAR, and TerraSAR-X validate the robustness and reliability of the proposed indicator. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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24 pages, 7793 KiB  
Article
Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems
by David N. Conran, Emmett J. Ientilucci, Timothy D. Bauch and Nina G. Raqueno
Remote Sens. 2024, 16(11), 1919; https://doi.org/10.3390/rs16111919 - 27 May 2024
Viewed by 999
Abstract
Hyperspectral imaging systems frequently rely on spectral rather than spatial resolving power for identifying objects within a scene. A hyperspectral imaging system’s response to point targets under flight conditions provides a novel technique for extracting system-level radiometric performance that is comparable to spatially [...] Read more.
Hyperspectral imaging systems frequently rely on spectral rather than spatial resolving power for identifying objects within a scene. A hyperspectral imaging system’s response to point targets under flight conditions provides a novel technique for extracting system-level radiometric performance that is comparable to spatially unresolved objects.The system-level analysis not only provides a method for verifying radiometric calibration during flight but also allows for the exploration of the impacts on small target radiometry, post orthorectification. Standard Lambertian panels do not provide similar insight due to the insensitivity of orthorectification over a uniform area. In this paper, we utilize a fixed mounted hyperspectral imaging system (radiometrically calibrated) to assess eight individual point targets over 18 drone flight overpasses. Of the 144 total observations, only 18.1% or 26 instances are estimated to be within the uncertainty of the predicted entrance aperture-reaching radiance signal. For completeness, the repeatability of Lambertian and point targets are compared over the 18 overpasses, where the effects of orthorectification drastically impact the radiometric estimate of point targets. The unique characteristic that point targets offer, being both a known spatial and radiometric source, is that they are the only field-deployable method for understanding the small target radiometric performance of drone-based hyperspectral imaging systems. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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22 pages, 6272 KiB  
Article
Modeling and Locating the Wind Erosion at the Dry Bottom of the Aral Sea Based on an InSAR Temporal Decorrelation Decomposition Model
by Yubin Song, Xuelian Xun, Hongwei Zheng, Xi Chen, Anming Bao, Ying Liu, Geping Luo, Jiaqiang Lei, Wenqiang Xu, Tie Liu, Olaf Hellwich and Qing Guan
Remote Sens. 2024, 16(10), 1800; https://doi.org/10.3390/rs16101800 - 18 May 2024
Viewed by 1219
Abstract
The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators [...] Read more.
The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators reported in this current study can accurately describe and measure wind erosion intensity. A novel wind erosion intensity (WEI) of a pixel resolution unit was defined in this paper based on deformation due to the wind erosion in this pixel resolution unit. We also derived the relationship between WEI and soil InSAR temporal decorrelation (ITD). ITD is usually caused by the surface change over time, which is very suitable for describing wind erosion. However, within a pixel resolution unit, the ITD signal usually includes soil and vegetation contributions, and extant studies concerning this issue are considerably limited. Therefore, we proposed an ITD decomposition model (ITDDM) to decompose the ITD signal of a pixel resolution unit. The least-square method (LSM) based on singular value decomposition (SVD) is used to estimate the ITD of soil (SITD) within a pixel resolution unit. We verified the results qualitatively by the landscape photos, which can reflect the actual conditions of the soil. At last, the WEI of the Aral Sea from 23 June 2020, to 5 July 2020 was mapped. The results confirmed that (1) based on the ITDDM model, the SITD can be accurately estimated by the LSM; (2) the Aral Sea is experiencing severe wind erosion; and (3) the middle, northeast, and southeast bare areas of the South Aral Sea are where salt dust storms may occur. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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25 pages, 10663 KiB  
Article
DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion
by Qiancheng Wei, Ying Liu, Xiaoping Jiang, Ben Zhang, Qiya Su and Muyao Yu
Remote Sens. 2024, 16(10), 1795; https://doi.org/10.3390/rs16101795 - 18 May 2024
Cited by 1 | Viewed by 1058
Abstract
The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each [...] Read more.
The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each modality (modality-distinctiveness) equally during processing, neglecting their distinct characteristics. Therefore, we propose a DDFNet-A for infrared and visible image fusion. DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. The extracted low and high features were then fused using distinct methods. In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network. DDFNet-A achieved competitive results on three datasets, with EN, MI, VIFF, QAB/F, FMI, and Qs metrics reaching the best performance on the TNO dataset, achieving 7.1217, 2.1620, 0.7739, 0.5426, 0.8129, and 0.9079, respectively. These values are 2.06%, 11.95%, 21.04%, 21.52%, 1.04%, and 0.09% higher than those of the second-best methods, respectively. The experimental results confirm that our DDFNet-A achieves better fusion performance than state-of-the-art (SOTA) methods. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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32 pages, 14542 KiB  
Article
Hyperspectral Image Mixed Noise Removal via Double Factor Total Variation Nonlocal Low-Rank Tensor Regularization
by Yongjie Wu, Wei Xu and Liangliang Zheng
Remote Sens. 2024, 16(10), 1686; https://doi.org/10.3390/rs16101686 - 9 May 2024
Cited by 1 | Viewed by 1097
Abstract
A hyperspectral image (HSI) is often corrupted by various types of noise during image acquisition, e.g., Gaussian noise, impulse noise, stripes, deadlines, and more. Thus, as a preprocessing step, HSI denoising plays a vital role in many subsequent tasks. Recently, a variety of [...] Read more.
A hyperspectral image (HSI) is often corrupted by various types of noise during image acquisition, e.g., Gaussian noise, impulse noise, stripes, deadlines, and more. Thus, as a preprocessing step, HSI denoising plays a vital role in many subsequent tasks. Recently, a variety of mixed noise removal approaches have been developed for HSI, and the methods based on spatial–spectral double factor and total variation (DFTV) regularization have achieved comparable performance. Additionally, the nonlocal low-rank tensor model (NLR) is often employed to characterize spatial nonlocal self-similarity (NSS). Generally, fully exploring prior knowledge can improve the denoising performance, but it significantly increases the computational cost when the NSS prior is employed. To solve this problem, this article proposes a novel DFTV-based NLR regularization (DFTVNLR) model for HSI mixed noise removal. The proposed model employs low-rank tensor factorization (LRTF) to characterize the spectral global low-rankness (LR), introduces 2-D and 1-D TV constraints on double-factor to characterize the spatial and spectral local smoothness (LS), respectively. Meanwhile, the NLR is applied to the spatial factor to characterize the NSS. Then, we developed an algorithm based on proximal alternating minimization (PAM) to solve the proposed model effectively. Particularly, we effectively controlled the computational cost from two aspects, namely taking small-sized double factor as regularization object and putting the time-consuming NLR model before the main loop with fewer iterations to solve it independently. Finally, considerable experiments on simulated and real noisy HSI substantiate that the proposed method is superior to the related state-of-the-art methods in balancing the denoising effect and speed. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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22 pages, 12352 KiB  
Article
Potential of Lightweight Drones and Object-Oriented Image Segmentation in Forest Plantation Assessment
by Jitendra Dixit, Ashok Kumar Bhardwaj, Saurabh Kumar Gupta, Suraj Kumar Singh, Gowhar Meraj, Pankaj Kumar, Shruti Kanga, Saurabh Singh and Bhartendu Sajan
Remote Sens. 2024, 16(9), 1554; https://doi.org/10.3390/rs16091554 - 27 Apr 2024
Viewed by 1129
Abstract
Forests play a vital role in maintaining ecological balance and provide numerous benefits. The monitoring and managing of large-scale forest plantations can be challenging and expensive. In recent years, advancements in remote sensing technologies, such as lightweight drones and object-oriented image analysis, have [...] Read more.
Forests play a vital role in maintaining ecological balance and provide numerous benefits. The monitoring and managing of large-scale forest plantations can be challenging and expensive. In recent years, advancements in remote sensing technologies, such as lightweight drones and object-oriented image analysis, have opened up new possibilities for efficient and accurate forest plantation monitoring. This study aimed to explore the utility of lightweight drones as a cost-effective and accurate method for mapping plantation characteristics in two 50 ha forest plots in the Nayla Range, Jaipur. By combining aerial photographs collected by the drone with photogrammetry and limited ground survey data, as well as topography and edaphic variables, this study examined the relative contribution of drone-derived plantation canopy information. The results demonstrate the immense potential of lightweight drones and object-oriented image analysis in providing valuable insights for optimizing silvicultural operations and planting trees in complex forest environments. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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19 pages, 2897 KiB  
Article
Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors
by Ziming Ni, Xianzhong Chen, Qingwen Hou and Jie Zhang
Remote Sens. 2024, 16(9), 1509; https://doi.org/10.3390/rs16091509 - 25 Apr 2024
Viewed by 699
Abstract
The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna [...] Read more.
The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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16 pages, 10343 KiB  
Article
Solar Wind Charge-Exchange X-ray Emissions from the O5+ Ions in the Earth’s Magnetosheath
by Zhicheng Zhang, Fei He, Xiao-Xin Zhang, Guiyun Liang, Xueyi Wang and Yong Wei
Remote Sens. 2024, 16(9), 1480; https://doi.org/10.3390/rs16091480 - 23 Apr 2024
Viewed by 748
Abstract
The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. Solar wind O6+ ions, which are the [...] Read more.
The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. Solar wind O6+ ions, which are the primary charge state for oxygen ions in solar wind, are considered. The line emissivity of the charge-exchange-borne O5+ ions is calculated by the Spectral Analysis System for Astrophysical and Laboratory (SASAL). It is found that the emission lines from O5+ range from 105.607 to 118.291 eV with a strong line at 107.047 eV. We then simulate the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of the north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm−2 s−1 sr−1 on the dayside, which is 4.5 times that before the solar wind structure reached the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at the north pole. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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29 pages, 17809 KiB  
Article
Revealing Decadal Glacial Changes and Lake Evolution in the Cordillera Real, Bolivia: A Semi-Automated Landsat Imagery Analysis
by Yilin Huang and Tsuyoshi Kinouchi
Remote Sens. 2024, 16(7), 1231; https://doi.org/10.3390/rs16071231 - 31 Mar 2024
Viewed by 1176
Abstract
The impact of global climate change on glaciers has drawn significant attention; however, limited research has been conducted to comprehend the consequences of glacier melting on the associated formation and evolution of glacial lakes. This study presents a semi-automated methodology developed on the [...] Read more.
The impact of global climate change on glaciers has drawn significant attention; however, limited research has been conducted to comprehend the consequences of glacier melting on the associated formation and evolution of glacial lakes. This study presents a semi-automated methodology developed on the cloud platforms Google Earth Engine and Google Colab to effectively detect dynamic changes in the glaciers as well as glacial and non-glacial lakes of the Cordillera Real, Bolivia, using over 200 Landsat images from 1984 to 2021. We found that the study area experienced a rise in temperature and precipitation, resulting in a substantial decline in glacier coverage and a simultaneous increase in both the total number and total area of lakes. A strong correlation between glacier area and the extent of natural glacier-fed lakes highlights the significant downstream impact of glacier recession on water bodies. Over the study period, glaciers reduced their total area by 42%, with recent years showing a deceleration in glacier recession, aligning with the recent stabilization observed in the area of natural glacier-fed lakes. Despite these overall trends, many smaller lakes, especially non-glacier-fed ones, decreased in size, attributed to seasonal and inter-annual variations in lake inflow caused by climate variability. These findings suggest the potential decline of natural lakes amid ongoing climate changes, prompting alterations in natural landscapes and local water resources. The study reveals the response of glaciers and lakes to climate variations, including the contribution of human-constructed water reservoirs, providing valuable insights into crucial aspects of future water resources in the Cordillera Real. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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29 pages, 11112 KiB  
Article
Analysing the Relationship between Spatial Resolution, Sharpness and Signal-to-Noise Ratio of Very High Resolution Satellite Imagery Using an Automatic Edge Method
by Valerio Pampanoni, Fabio Fascetti, Luca Cenci, Giovanni Laneve, Carla Santella and Valentina Boccia
Remote Sens. 2024, 16(6), 1041; https://doi.org/10.3390/rs16061041 - 15 Mar 2024
Cited by 2 | Viewed by 1629
Abstract
Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of [...] Read more.
Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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24 pages, 4327 KiB  
Article
Dynamic Spatial–Spectral Feature Optimization-Based Point Cloud Classification
by Yali Zhang, Wei Feng, Yinghui Quan, Guangqiang Ye and Gabriel Dauphin
Remote Sens. 2024, 16(3), 575; https://doi.org/10.3390/rs16030575 - 2 Feb 2024
Viewed by 1376
Abstract
With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existing point cloud classification algorithms have problems such as high computational [...] Read more.
With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existing point cloud classification algorithms have problems such as high computational complexity, a lack of feature optimization, and low classification accuracy. This paper proposes an efficient point cloud classification algorithm based on dynamic spatial–spectral feature optimization. It can eliminate redundant features, optimize features, reduce computational costs, and improve classification accuracy. It achieves feature optimization through three key steps. First, the proposed method extracts spatial, geometric, spectral, and other features from point cloud data. Then, the Gini index and Fisher score are used to calculate the importance and relevance of features, and redundant features are filtered. Finally, feature importance factors are used to dynamically enhance the discriminative power of highly distinguishable features to strengthen their contribution to point cloud classification. Four real-scene datasets from STPLS3D are utilized for experimentation. Compared to the other five algorithms, the proposed algorithm achieves at least a 37.97% improvement in mean intersection over union (mIoU). Meanwhile, the results indicate that the proposed algorithm can achieve high-precision point cloud classification with low computational complexity. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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24 pages, 8438 KiB  
Article
Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices
by Sarah Farhadpour, Timothy A. Warner and Aaron E. Maxwell
Remote Sens. 2024, 16(3), 533; https://doi.org/10.3390/rs16030533 - 30 Jan 2024
Cited by 4 | Viewed by 2129
Abstract
Evaluating classification accuracy is a key component of the training and validation stages of thematic map production, and the choice of metric has profound implications for both the success of the training process and the reliability of the final accuracy assessment. We explore [...] Read more.
Evaluating classification accuracy is a key component of the training and validation stages of thematic map production, and the choice of metric has profound implications for both the success of the training process and the reliability of the final accuracy assessment. We explore key considerations in selecting and interpreting loss and assessment metrics in the context of data imbalance, which arises when the classes have unequal proportions within the dataset or landscape being mapped. The challenges involved in calculating single, integrated measures that summarize classification success, especially for datasets with considerable data imbalance, have led to much confusion in the literature. This confusion arises from a range of issues, including a lack of clarity over the redundancy of some accuracy measures, the importance of calculating final accuracy from population-based statistics, the effects of class imbalance on accuracy statistics, and the differing roles of accuracy measures when used for training and final evaluation. In order to characterize classification success at the class level, users typically generate averages from the class-based measures. These averages are sometimes generated at the macro-level, by taking averages of the individual-class statistics, or at the micro-level, by aggregating values within a confusion matrix, and then, calculating the statistic. We show that the micro-averaged producer’s accuracy (recall), user’s accuracy (precision), and F1-score, as well as weighted macro-averaged statistics where the class prevalences are used as weights, are all equivalent to each other and to the overall accuracy, and thus, are redundant and should be avoided. Our experiment, using a variety of loss metrics for training, suggests that the choice of loss metric is not as complex as it might appear to be, despite the range of choices available, which include cross-entropy (CE), weighted CE, and micro- and macro-Dice. The highest, or close to highest, accuracies in our experiments were obtained by using CE loss for models trained with balanced data, and for models trained with imbalanced data, the highest accuracies were obtained by using weighted CE loss. We recommend that, since weighted CE loss used with balanced training is equivalent to CE, weighted CE loss is a good all-round choice. Although Dice loss is commonly suggested as an alternative to CE loss when classes are imbalanced, micro-averaged Dice is similar to overall accuracy, and thus, is particularly poor for training with imbalanced data. Furthermore, although macro-Dice resulted in models with high accuracy when the training used balanced data, when the training used imbalanced data, the accuracies were lower than for weighted CE. In summary, the significance of this paper lies in its provision of readers with an overview of accuracy and loss metric terminology, insight regarding the redundancy of some measures, and guidance regarding best practices. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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19 pages, 4581 KiB  
Article
An Efficient Rep-Style Gaussian–Wasserstein Network: Improved UAV Infrared Small Object Detection for Urban Road Surveillance and Safety
by Tuerniyazi Aibibu, Jinhui Lan, Yiliang Zeng, Weijian Lu and Naiwei Gu
Remote Sens. 2024, 16(1), 25; https://doi.org/10.3390/rs16010025 - 20 Dec 2023
Cited by 5 | Viewed by 1665
Abstract
Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect small road objects 24 h/day, this study proposes an efficient Rep-style Gaussian–Wasserstein [...] Read more.
Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect small road objects 24 h/day, this study proposes an efficient Rep-style Gaussian–Wasserstein network (ERGW-net) for small road object detection in infrared aerial images. This method aims to resolve problems of small object size, low contrast, few object features, and occlusions. The ERGW-net adopts the advantages of ResNet, Inception net, and YOLOv8 networks to improve object detection efficiency and accuracy by improving the structure of the backbone, neck, and loss function. The ERGW-net was tested on a DroneVehicle dataset with a large sample size and the HIT-UAV dataset with a relatively small sample size. The results show that the detection accuracy of different road targets (e.g., pedestrians, cars, buses, and trucks) is greater than 80%, which is higher than the existing methods. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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28 pages, 4722 KiB  
Article
Hyperspectral Target Detection Methods Based on Statistical Information: The Key Problems and the Corresponding Strategies
by Luyan Ji and Xiurui Geng
Remote Sens. 2023, 15(15), 3835; https://doi.org/10.3390/rs15153835 - 1 Aug 2023
Cited by 1 | Viewed by 1550
Abstract
Target detection is an important area in the applications of hyperspectral remote sensing. Due to the full use of information of the target and background, target detection algorithms based on the statistical characteristics of an image are always occupy a dominant position in [...] Read more.
Target detection is an important area in the applications of hyperspectral remote sensing. Due to the full use of information of the target and background, target detection algorithms based on the statistical characteristics of an image are always occupy a dominant position in the field of hyperspectral target detection. From the perspective of statistical information, we firstly presented detailed discussions on the key factors affecting the target detection results, including data origin, target size, spectral variability of target, and the number of bands. Further, we gave the corresponding strategies for several common situations in the practical target detection applications. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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