Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics
Impact Factor:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau
Remote Sens. 2024, 16(8), 1454; https://doi.org/10.3390/rs16081454 (registering DOI) - 19 Apr 2024
Abstract
Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the
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Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns.
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(This article belongs to the Section Environmental Remote Sensing)
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Open AccessArticle
Time–Frequency Signal Integrity Monitoring Algorithm Based on Temperature Compensation Frequency Bias Combination Model
by
Yu Guo, Zongnan Li, Hang Gong, Jing Peng and Gang Ou
Remote Sens. 2024, 16(8), 1453; https://doi.org/10.3390/rs16081453 - 19 Apr 2024
Abstract
To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring,
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To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring, with their time differences and frequency biases serving as essential indicators. These indicators are influenced by the inherent characteristics of the time–frequency signals, as well as the links and equipment they traverse. Meanwhile, existing research primarily focuses on only monitoring the integrity of the time–frequency signals’ output by the atomic clock group, neglecting the integrity monitoring of the time–frequency signals generated and distributed by the time–frequency signal generation and distribution subsystem. This paper introduces a time–frequency signal integrity monitoring algorithm based on the temperature compensation frequency bias combination model. By analyzing the characteristics of time difference measurements, constructing the temperature compensation frequency bias combination model, and extracting and monitoring noise and frequency bias features from the time difference measurements, the algorithm achieves comprehensive time–frequency signal integrity monitoring. Experimental results demonstrate that the algorithm can effectively detect, identify, and alert users to time–frequency signal faults. Additionally, the model and the integrity monitoring parameters developed in this paper exhibit high adaptability, making them directly applicable to the integrity monitoring of time–frequency signals across various links. Compared with traditional monitoring algorithms, the algorithm proposed in this paper greatly improves the effectiveness, adaptability, and real-time performance of time–frequency signal integrity monitoring.
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Open AccessArticle
InSAR Digital Elevation Model Void-Filling Method Based on Incorporating Elevation Outlier Detection
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Zhi Hu, Rong Gui, Jun Hu, Haiqiang Fu, Yibo Yuan, Kun Jiang and Liqun Liu
Remote Sens. 2024, 16(8), 1452; https://doi.org/10.3390/rs16081452 - 19 Apr 2024
Abstract
Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics,
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Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics, gaps often appear in regions marked by significant topographical fluctuations, necessitating an extra void-filling process. Traditional void-filling methods have operated directly on preexisting data, succeeding in relatively flat terrain. When facing mountainous regions, there will always be gross errors in elevation values. Regrettably, conventional methods have often disregarded this vital consideration. To this end, this research proposes a DEM void-filling method based on incorporating elevation outlier detection. It accounts for the detection and removal of elevation outliers, thereby mitigating the shortcomings of existing methods and ensuring robust DEM restoration in mountainous terrains. Experiments were conducted to validate the method applicability using TanDEM-X data from Sichuan, China, Hebei, China, and Oregon, America. The results underscore the superiority of the proposed method. Three traditional methods are selected for comparison. The proposed method has different degrees of improvement in filling accuracy, depending on the void status of the local terrain. Compared with the delta surface fill (DSF) method, the root mean squared error (RMSE) of the filling results has improved by 7.87% to 51.87%. The qualitative and quantitative experiments demonstrate that the proposed method is promising for large-scale DEM void-filling tasks.
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Open AccessArticle
Combining Multitemporal Optical and Radar Satellite Data for Mapping the Tatra Mountains Non-Forest Plant Communities
by
Marcin Kluczek, Bogdan Zagajewski and Marlena Kycko
Remote Sens. 2024, 16(8), 1451; https://doi.org/10.3390/rs16081451 - 19 Apr 2024
Abstract
Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central
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Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area.
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(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)
Open AccessArticle
GLUENet: An Efficient Network for Remote Sensing Image Dehazing with Gated Linear Units and Efficient Channel Attention
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Jiahao Fang, Xing Wang, Yujie Li, Xuefeng Zhang, Bingxian Zhang and Martin Gade
Remote Sens. 2024, 16(8), 1450; https://doi.org/10.3390/rs16081450 - 19 Apr 2024
Abstract
Dehazing individual remote sensing (RS) images is an effective approach to enhance the quality of hazy remote sensing imagery. However, current dehazing methods exhibit substantial systemic and computational complexity. Such complexity not only hampers the straightforward analysis and comparison of these methods but
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Dehazing individual remote sensing (RS) images is an effective approach to enhance the quality of hazy remote sensing imagery. However, current dehazing methods exhibit substantial systemic and computational complexity. Such complexity not only hampers the straightforward analysis and comparison of these methods but also undermines their practical effectiveness on actual data, attributed to the overtraining and overfitting of model parameters. To mitigate these issues, we introduce a novel dehazing network for non-uniformly hazy RS images: GLUENet, designed for both lightweightness and computational efficiency. Our approach commences with the implementation of the classical U-Net, integrated with both local and global residuals, establishing a robust base for the extraction of multi-scale information. Subsequently, we construct basic convolutional blocks using gated linear units and efficient channel attention, incorporating depth-separable convolutional layers to efficiently aggregate spatial information and transform features. Additionally, we introduce a fusion block based on efficient channel attention, facilitating the fusion of information from different stages in both encoding and decoding to enhance the recovery of texture details. GLUENet’s efficacy was evaluated using both synthetic and real remote sensing dehazing datasets, providing a comprehensive assessment of its performance. The experimental results demonstrate that GLUENet’s performance is on par with state-of-the-art (SOTA) methods and surpasses the SOTA methods on our proposed real remote sensing dataset. Our method on the real remote sensing dehazing dataset has an improvement of 0.31 dB for the PSNR metric and 0.13 for the SSIM metric, and the number of parameters and computations of the model are much lower than the optimal method.
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(This article belongs to the Section AI Remote Sensing)
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Bridging Domains and Resolutions: Deep Learning-Based Land Cover Mapping without Matched Labels
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Shuyi Cao, Yubin Tang, Enping Yan, Jiawei Jiang and Dengkui Mo
Remote Sens. 2024, 16(8), 1449; https://doi.org/10.3390/rs16081449 - 19 Apr 2024
Abstract
High-resolution land cover mapping is crucial in various disciplines but is often hindered by the lack of accurately matched labels. Our study introduces an innovative deep learning methodology for effective land cover mapping, independent of matched labels. The approach comprises three main components:
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High-resolution land cover mapping is crucial in various disciplines but is often hindered by the lack of accurately matched labels. Our study introduces an innovative deep learning methodology for effective land cover mapping, independent of matched labels. The approach comprises three main components: (1) An advanced fully convolutional neural network, augmented with super-resolution features, to refine labels; (2) The application of an instance-batch normalization network (IBN), leveraging these enhanced labels from the source domain, to generate 2-m resolution land cover maps for test sites in the target domain; (3) Noise assessment tests to evaluate the impact of varying noise levels on the model’s mapping accuracy using external labels. The model achieved an overall accuracy of 83.40% in the target domain using endogenous super-resolution labels. In contrast, employing exogenous, high-precision labels from the National Land Cover Database in the source domain led to a notable accuracy increase of 2.55%, reaching 85.48%. This improvement highlights the model’s enhanced generalizability and performance during domain shifts, attributed significantly to the IBN layer. Our findings reveal that, despite the absence of native high-precision labels, the utilization of high-quality external labels can substantially benefit the development of precise land cover mapping, underscoring their potential in scenarios with unmatched labels.
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(This article belongs to the Special Issue New Deep Learning Paradigms for Multisource Remote Sensing Data Fusion and Classification)
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Properties of Cirrus Cloud Observed over Koror, Palau (7.3°N, 134.5°E), in Tropical Western Pacific Region
by
Xiaoyu Sun, Christoph Ritter, Katrin Müller, Mathias Palm, Denghui Ji, Wilfried Ruhe, Ingo Beninga, Sharon Patris and Justus Notholt
Remote Sens. 2024, 16(8), 1448; https://doi.org/10.3390/rs16081448 - 19 Apr 2024
Abstract
This study presented an analysis of the geometric and optical properties of cirrus clouds with data produced by Compact Cloud-Aerosol Lidar (ComCAL) over Koror, Palau (7.3°N, 134.5°E), in the Tropical Western Pacific region. The lidar measurement dataset covers April 2018 to May 2019
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This study presented an analysis of the geometric and optical properties of cirrus clouds with data produced by Compact Cloud-Aerosol Lidar (ComCAL) over Koror, Palau (7.3°N, 134.5°E), in the Tropical Western Pacific region. The lidar measurement dataset covers April 2018 to May 2019 and includes data collected during March, July and August 2022. The results show that cirrus clouds occur approximately 47.9% of the lidar sampling time, predominantly between altitudes of 15 and 18 km. Seasonal variations in cirrus top height closely align with those of the cold point tropopause. Most cirrus clouds exhibit low cloud optical depth (COD < 0.1), with an annual mean depolarization ratio of 31 ± 19%. Convective-forming cirrus clouds during the summer monsoon season exhibit a larger size by notably lower values in terms of color ratio. Extremely thin cirrus clouds (COD < 0.005) constituting 1.6% of total cirrus occurrences are frequently observed at 1–2 km above the cold point, particularly during winter and summer, suggesting significant stratosphere–troposphere exchange. The coldest and highest tropopause over Palau is persistent during winter, and related to the pathway of tropospheric air entering the stratosphere through the cold trap. In summer, the extremely thin cirrus above the cold point is likely correlated with equatorial Kelvin waves induced by western Pacific monsoon convection.
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(This article belongs to the Section Atmospheric Remote Sensing)
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Canopy-Level Spectral Variation and Classification of Diverse Crop Species with Fine Spatial Resolution Imaging Spectroscopy
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Jie Dai, Marcel König, Elahe Jamalinia, Kelly L. Hondula, Nicholas R. Vaughn, Joseph Heckler and Gregory P. Asner
Remote Sens. 2024, 16(8), 1447; https://doi.org/10.3390/rs16081447 - 19 Apr 2024
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With the increasing availability and volume of remote sensing data, imaging spectroscopy is an expanding tool for agricultural studies. One of the fundamental applications in agricultural research is crop mapping and classification. Previous studies have mostly focused at local to regional scales, and
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With the increasing availability and volume of remote sensing data, imaging spectroscopy is an expanding tool for agricultural studies. One of the fundamental applications in agricultural research is crop mapping and classification. Previous studies have mostly focused at local to regional scales, and classifications were usually performed for a limited number of crop types. Leveraging fine spatial resolution (60 cm) imaging spectroscopy data collected by the Global Airborne Observatory (GAO), we investigated canopy-level spectral variations in 16 crop species from different agricultural regions in the U.S. Inter-specific differences were quantified through principal component analysis (PCA) of crop spectra and their Euclidean distances in the PC space. We also classified the crop species using support vector machines (SVM), demonstrating high classification accuracy with a test kappa of 0.97. A separate test with an independent dataset also returned high accuracy (kappa = 0.95). Classification using full reflectance spectral data (320 bands) and selected optimal wavebands from the literature resulted in similar classification accuracies. We demonstrated that classification involving diverse crop species is achievable, and we encourage further testing based on moderate spatial resolution imaging spectrometer data.
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Open AccessArticle
Characterization of River Width Measurement Capability by Space Borne GNSS-Reflectometry
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April Warnock, Christopher S. Ruf and Arie L. Knoll
Remote Sens. 2024, 16(8), 1446; https://doi.org/10.3390/rs16081446 - 19 Apr 2024
Abstract
In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area
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In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data are generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using this predictive relationship indicates that, for simplistic river scenes, the SNR over the river is predictive of the river width to within +/−5 m. The presence of clutter (surrounding water bodies) within ~500 m of a river causes perturbations in the SNR measured over the river, which can render the river width retrievals unreliable. The results of this study indicate that, for isolated, straight rivers, GNSS-R data are able to measure river widths as narrow as 160 m with ~3% error.
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(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)
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Potential Modulation of Aerosol on Precipitation Efficiency in Southwest China
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Pengguo Zhao, Xiaoran Liu and Chuanfeng Zhao
Remote Sens. 2024, 16(8), 1445; https://doi.org/10.3390/rs16081445 - 18 Apr 2024
Abstract
The aerosol–cloud–precipitation correlation has been a significant scientific topic, primarily due to its remarkable uncertainty. However, the possible modulation of aerosol on the precipitation capacity of clouds has received limited attention. In this study, we utilized multi-source data on aerosol, cloud properties, precipitation,
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The aerosol–cloud–precipitation correlation has been a significant scientific topic, primarily due to its remarkable uncertainty. However, the possible modulation of aerosol on the precipitation capacity of clouds has received limited attention. In this study, we utilized multi-source data on aerosol, cloud properties, precipitation, and meteorological factors to investigate the impact of aerosols on precipitation efficiency (PE) in the Sichuan Basin (SCB) and Yun-nan-Guizhou Plateau (YGP), where the differences between terrain and meteorological environment conditions were prominent. In the two study regions, there were significant negative correlations between the aerosol index (AI) and PE in spring, especially in the YGP, while the correlations between the AI and PE in other seasons were not as prominent as in spring. In spring, aerosol significantly inhibited both the liquid water path (LWP) and the ice water path (IWP) in the YGP, but negatively correlated with the IWP and had no significant relationship with the LWP in the SCB. Aerosol inhibited precipitation in the two regions mainly by reducing cloud droplet effective radius, indicating that warm clouds contributed more to precipitation in spring. The suppressive impact of aerosols on precipitation serving as the numerator of PE is greater than that of the cloud water path as the denominator of PE, resulting in a negative correlation between aerosol and PE. The AI–PE relationship is significantly dependent on meteorological conditions in the YGP, but not in the SCB, which may be related to the perennial cloud cover and stable atmosphere in the SCB. In the future, as air quality continues to improve, precipitation efficiency may increase due to the decrease in aerosol concentration, and of course, the spatio-temporal heterogeneity of the aerosol–cloud–precipitation relationship may become more significant.
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(This article belongs to the Special Issue Application of Satellite Aerosol Remote Sensing in Air Quality)
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Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification
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Yuanbing Lu, Huapeng Li, Ce Zhang and Shuqing Zhang
Remote Sens. 2024, 16(8), 1444; https://doi.org/10.3390/rs16081444 - 18 Apr 2024
Abstract
Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries.
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Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment’s probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification.
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(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)
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A Multiscale Filtering Method for Airborne LiDAR Data Using Modified 3D Alpha Shape
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Di Cao, Cheng Wang, Meng Du and Xiaohuan Xi
Remote Sens. 2024, 16(8), 1443; https://doi.org/10.3390/rs16081443 - 18 Apr 2024
Abstract
The complexity of terrain features poses a substantial challenge in the effective processing and application of airborne LiDAR data, particularly in regions characterized by steep slopes and diverse objects. In this paper, we propose a novel multiscale filtering method utilizing a modified 3D
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The complexity of terrain features poses a substantial challenge in the effective processing and application of airborne LiDAR data, particularly in regions characterized by steep slopes and diverse objects. In this paper, we propose a novel multiscale filtering method utilizing a modified 3D alpha shape algorithm to increase the ground point extraction accuracy in complex terrain. Our methodology comprises three pivotal stages: preprocessing for outlier removal and potential ground point extraction; the deployment of a modified 3D alpha shape to construct multiscale point cloud layers; and the use of a multiscale triangulated irregular network (TIN) densification process for precise ground point extraction. In each layer, the threshold is adaptively determined based on the corresponding . Points closer to the TIN surface than the threshold are identified as ground points. The performance of the proposed method was validated using a classical benchmark dataset provided by the ISPRS and an ultra-large-scale ground filtering dataset called OpenGF. The experimental results demonstrate that this method is effective, with an average total error and a kappa coefficient on the ISPRS dataset of 3.27% and 88.97%, respectively. When tested in the large scenarios of the OpenGF dataset, the proposed method outperformed four classical filtering methods and achieved accuracy comparable to that of the best of learning-based methods.
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(This article belongs to the Special Issue Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution)
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Open AccessArticle
Assessing Greenhouse Gas Monitoring Capabilities Using SolAtmos End-to-End Simulator: Application to the Uvsq-Sat NG Mission
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Cannelle Clavier, Mustapha Meftah, Alain Sarkissian, Frédéric Romand, Odile Hembise Fanton d’Andon, Antoine Mangin, Slimane Bekki, Pierre-Richard Dahoo, Patrick Galopeau, Franck Lefèvre, Alain Hauchecorne and Philippe Keckhut
Remote Sens. 2024, 16(8), 1442; https://doi.org/10.3390/rs16081442 - 18 Apr 2024
Abstract
Monitoring atmospheric concentrations of greenhouse gases (GHGs) like carbon dioxide and methane in near real time and with good spatial resolution is crucial for enhancing our understanding of the sources and sinks of these gases. A novel approach can be proposed using a
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Monitoring atmospheric concentrations of greenhouse gases (GHGs) like carbon dioxide and methane in near real time and with good spatial resolution is crucial for enhancing our understanding of the sources and sinks of these gases. A novel approach can be proposed using a constellation of small satellites equipped with miniaturized spectrometers having a spectral resolution of a few nanometers. The objective of this study is to describe expected results that can be obtained with a single satellite named Uvsq-Sat NG. The SolAtmos end-to-end simulator and its three tools (IRIS, OptiSpectra, and GHGRetrieval) were developed to evaluate the performance of the spectrometer of the Uvsq-Sat NG mission, which focuses on measuring the main GHGs. The IRIS tool was implemented to provide Top-Of-Atmosphere (TOA) spectral radiances. Four scenes were analyzed (pine forest, deciduous forest, ocean, snow) combined with different aerosol types (continental, desert, maritime, urban). Simulated radiance spectra were calculated based on the wavelength ranges of the Uvsq-Sat NG, which spans from 1200 to 2000 nm. The OptiSpectra tool was used to determine optimal observational settings for the spectrometer, including Signal-to-Noise Ratio (SNR) and integration time. Data derived from IRIS and OptiSpectra served as input for our GHGRetrieval simulation tool, developed to provide greenhouse gas concentrations. The Levenberg–Marquardt algorithm was applied iteratively to fine-tune gas concentrations and model inputs, aligning observed transmittance functions with simulated ones under given environmental conditions. To estimate gas concentrations (CO2, CH4, O2, H2O) and their uncertainties, the Monte Carlo method was used. Based on this analysis, this study demonstrates that a miniaturized spectrometer onboard Uvsq-Sat NG is capable of observing different scenes by adjusting its integration time according to the wavelength. The expected precision for each measurement is of the order of a few ppm for carbon dioxide and less than 25 ppb for methane.
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(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions II)
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A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing
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Tiejian Tang, Jinghao Cao, Xiong Yang, Sheng Liu, Dongsheng Zhu, Sidan Du and Yang Li
Remote Sens. 2024, 16(8), 1441; https://doi.org/10.3390/rs16081441 - 18 Apr 2024
Abstract
Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train’s operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lacks
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Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train’s operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lacks essential 3D environmental information about the train, while the LiDAR-based approach tends to detect tracks of insufficient length due to the inherent limitations of LiDAR. In this study, we propose a real-time method for railway track detection and 3D fitting based on camera and LiDAR fusion sensing. Semantic segmentation of the railway track in the image is performed, followed by inverse projection to obtain 3D information of the distant railway track. Then, 3D fitting is applied to the inverse projection of the railway track for track vectorization and LiDAR railway track point segmentation. The extrinsic parameters necessary for inverse projection are continuously optimized to ensure robustness against variations in extrinsic parameters during the train’s operation. Experimental results show that the proposed method achieves desirable accuracy for railway track detection and 3D fitting with acceptable computational efficiency, and outperforms existing approaches based on LiDAR, camera, and camera–LiDAR fusion. To the best of our knowledge, our approach represents the first successful attempt to fuse camera and LiDAR data for real-time railway track detection and 3D fitting.
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(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)
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Studying the Internal Wave Generation Mechanism in the Northern South China Sea Using Numerical Simulation, Synthetic Aperture Radar, and In Situ Measurements
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Kan Zeng, Ruyin Lyu, Hengyu Li, Rongqing Suo, Tao Du and Mingxia He
Remote Sens. 2024, 16(8), 1440; https://doi.org/10.3390/rs16081440 - 18 Apr 2024
Abstract
The internal waves in the South China Sea are highly correlated with the tidal currents in the Luzon Strait, which makes it possible to establish an internal wave prediction model based on internal wave kinematics. However, the kinematic model requires the input of
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The internal waves in the South China Sea are highly correlated with the tidal currents in the Luzon Strait, which makes it possible to establish an internal wave prediction model based on internal wave kinematics. However, the kinematic model requires the input of the exact location and time of the initial internal wave for which the generation mechanism of internal waves in the northern South China Sea must be well understood. By analyzing the internal wave field in the northern South China Sea (SCS) simulated using the MIT General Circulation Model (MITgcm) and observations from satellite synthetic aperture radar (SAR) and mooring temperature–salinity–depth (TSD) chains, the source regions and propagation initiation times of internal waves are identified for three typical tidal phases, i.e., the diurnal-tide-dominated phase (DTP), transition tide phase (TTP), and semidiurnal-tide-dominated phase (STP). The generation procedures of Type A and Type B internal waves are discussed in detail with those data. The present study reveals that Type A and Type B waves are generated at the eastern and western ridges, respectively, and both commence their westward propagation at the peak of the eastward tidal flow. The dynamics of lee waves and the resonance effect with double ridges constitute the generation mechanisms of internal waves in the northern SCS. Combined with varying configurations of tidal conditions, topography, and stratification, the generation procedures of Type A and Type B waves in the DTP, TTP, and STP are elucidated with the generation mechanism in a unified and self-consistent way. In short, during DTP, weaker A waves alternate with weaker B waves each day; during TTP, strong A waves and strong B waves appear alternately every day; and there are two weak A waves per day during the STP. The generation mechanism can help in developing future empirical models for generating internal waves using tidal currents, topography, and stratification without requiring complex fluid dynamics calculations.
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(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation II)
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Detecting Moving Wildlife Using the Time Difference between Two Thermal Airborne Images
by
Yu Oishi, Natsuki Yoshida and Hiroyuki Oguma
Remote Sens. 2024, 16(8), 1439; https://doi.org/10.3390/rs16081439 - 18 Apr 2024
Abstract
Wildlife damage to agriculture is serious in Japan; therefore, it is important to understand changes in wildlife population sizes. Although several studies have been conducted to detect wildlife from drone images, behavioral changes (such as wildlife escaping when a drone approaches) have been
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Wildlife damage to agriculture is serious in Japan; therefore, it is important to understand changes in wildlife population sizes. Although several studies have been conducted to detect wildlife from drone images, behavioral changes (such as wildlife escaping when a drone approaches) have been confirmed. To date, the use of visible and near-infrared images has been limited to the daytime because many large mammals, such as sika deer (Cervus nippon), are crepuscular. However, it is difficult to detect wildlife in the thermal images of urban areas that are not open and contain various heat spots. To address this issue, a method was developed in a previous study to detect moving wildlife using pairs of time-difference thermal images. However, the user’s accuracy was low. In the current study, two methods are proposed for extracting moving wildlife using pairs of airborne thermal images and deep learning models. The first method was to judge grid areas with wildlife using a deep learning classification model. The second method detected each wildlife species using a deep learning object detection model. The proposed methods were then applied to pairs of airborne thermal images. The classification test accuracies of “with deer” and “without deer” were >85% and >95%, respectively. The average precision of detection, precision, and recall were >85%. This indicates that the proposed methods are practically accurate for monitoring changes in wildlife populations and can reduce the person-hours required to monitor a large number of thermal remote-sensing images. Therefore, efforts should be made to put these materials to practical use.
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(This article belongs to the Special Issue Advancements in AI-Based Remote Sensing Object Detection)
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Exploring Spatiotemporal Characteristics and Driving Forces of Straw Burning in Hunan Province, China, from 2010 to 2020
by
Yu Zeng, Shuguang Liu, Sheng Huang, Sopan D. Patil, Wenyuan Gao and Hao Li
Remote Sens. 2024, 16(8), 1438; https://doi.org/10.3390/rs16081438 - 18 Apr 2024
Abstract
Straw burning is a significant source of atmospheric pollutants, releasing particulate matter and trace gases. Capturing the characteristics of straw burning and understanding its influencing factors are important prerequisites for regulating straw burning. Based on the fire points detected by the Moderate-resolution Imaging
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Straw burning is a significant source of atmospheric pollutants, releasing particulate matter and trace gases. Capturing the characteristics of straw burning and understanding its influencing factors are important prerequisites for regulating straw burning. Based on the fire points detected by the Moderate-resolution Imaging Spectroradiometer (MODIS) in Hunan province, China, from 2010 to 2020, this study analyzed the spatiotemporal variations of straw burning and employed Geographically Weighted Regression (GWR) models to investigate the underlying forces driving straw burning. The results show that the spatiotemporal characteristics of straw burning in Hunan Province can be categorized into two distinct periods: 2010 to 2014 and 2015 to 2020. The period from 2010 to 2014 witnessed a rapid increase, while the period from 2015 to 2020 experienced fluctuating decreases. The shift is closely linked to the implementation of the straw burning ban policy in 2015. Spatially, the areas with a high number of fire points are primarily located in the southern regions, especially in the border regions between Chenzhou, Yongzhou, Hengyang, and Shaoyang cities. A significant change was found in the impact of economic development and human activity factors on straw burning before and after 2015. These factors include crop yield, Gross Domestic Product (GDP), and road network development. From the implementation of the straw burning ban policy, increases in GDP and settlement density will have a dampening effect on straw burning in a region. Straw burning locations may shift towards regions with relatively slow economic development. The results could serve as a foundation for decision-making to address the issue of straw burning.
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(This article belongs to the Section Environmental Remote Sensing)
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Changes in Surface and Terrestrial Waters in the China–Pakistan Economic Corridor Due to Climate Change and Human Activities
by
Jiayu Bao, Yanfeng Wu, Xiaoran Huang, Peng Qi, Ye Yuan, Tao Li, Tao Yu, Ting Wang, Pengfei Zhang, Vincent Nzabarinda, Sulei Naibi, Jingyu Jin, Gang Long and Shuya Yang
Remote Sens. 2024, 16(8), 1437; https://doi.org/10.3390/rs16081437 - 18 Apr 2024
Abstract
The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within
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The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within the context of the CPEC. To fill this gap, we first analyzed the annual changes in the SWA and TWS in the China–Pakistan Economic Corridor (CPEC) region in recent decades using the methods of correlation analysis and Geodetector. Our findings indicate that Sindh exhibited the highest increase in the SWA at 8.68 ha/km2, whereas FATA showed the least increase at 0.2 ha/km2 from 2002 to 2018. Punjab exhibited a significant decrease in TWS, with a slope of −0.48 cm/year. Azad Kashmir followed with a decrease in TWS at a rate of −0.36 cm/year. Khyber Pakhtunkhwa and FATA exhibited an insignificant increase in TWS, with values of 0.02 cm/year and 0.11 cm/year, respectively. TWS was significantly positively correlated with the SWA in Balochistan and Khyber Pakhtunkhwa. However, other regions showed inconsistent changes; in particular, a decline was observed in Gilgit–Baltistan. The changes in TWS in Balochistan were primarily influenced by the SWA and climate change, while TWS changes in FATA were mainly affected by climate change. In addition, human activities had a primary impact on the TWS changes in Azad Kashmir, Punjab, and Sindh. The influencing factors of TWS changes in different regions of the CPEC mainly involved a dual-factor enhancement and the nonlinear weakening of single factors. These results highlight that under the effect of climate change and human activities, TWS may not increase as surface water area increases. This study contributes to a better understanding of water resource dynamics and can aid in the development of strategies for the efficient and sustainable use of water resources in the CPEC.
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(This article belongs to the Special Issue Earth Observation in Support of Sustainable Water Resources Management)
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Sun/Shade Separation in Optical and Thermal UAV Images for Assessing the Impact of Agricultural Practices
by
Claire Marais-Sicre, Solen Queguiner, Vincent Bustillo, Luka Lesage, Hugues Barcet, Nathalie Pelle, Nicolas Breil and Benoit Coudert
Remote Sens. 2024, 16(8), 1436; https://doi.org/10.3390/rs16081436 - 18 Apr 2024
Abstract
Unmanned aerial vehicles (UAVs) provide images at decametric spatial resolutions. Their flexibility, efficiency, and low cost make it possible to apply UAV remote sensing to multisensor data acquisition. In this frame, the present study aims at employing RGB UAV images (at a 3
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Unmanned aerial vehicles (UAVs) provide images at decametric spatial resolutions. Their flexibility, efficiency, and low cost make it possible to apply UAV remote sensing to multisensor data acquisition. In this frame, the present study aims at employing RGB UAV images (at a 3 cm resolution) and multispectral images (at a 16 cm resolution) with related vegetation indices (VIs) for mapping surfaces according to their illumination. The aim is to map land cover in order to access temperature distribution and compare NDVI and MTVI2 dynamics as a function of their illuminance. The method, which is based on a linear discriminant analysis, is validated at different periods during the phenological cycle of the crops in place. A model based on a given date is evaluated, as well as the use of a generic model. The method provides a good capacity of separation between four classes: vegetation, no-vegetation, shade, and sun (average kappa of 0.93). The effects of agricultural practices on two adjacent plots of maize respectively submitted to conventional and conservation farming are assessed. The transition from shade to sun increases the brightness temperature by 2.4 °C and reduces the NDVI by 26% for non-vegetated surfaces. The conservation farming plot is found to be 1.9 °C warmer on the 11th of July 2019, with no significant difference between vegetation in the sun or shade. The results also indicate that the NDVI of non-vegetated areas is increased by the presence of crop residues on the conservation agriculture plot and by the effect of shade on the conventional plot which is different for MTVI2.
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(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Raster Scale Farmland Productivity Assessment with Multi-Source Data Fusion—A Case of Typical Black Soil Region in Northeast China
by
Yuwen Liu, Chengyuan Wang, Enheng Wang, Xuegang Mao, Yuan Liu and Zhibo Hu
Remote Sens. 2024, 16(8), 1435; https://doi.org/10.3390/rs16081435 - 18 Apr 2024
Abstract
Degradation of black soil areas is a serious threat to national food security and ecological safety; nevertheless, the current lack of information on the location, size, and condition of black soil farmland productivity is a major obstacle to the development of strategies for
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Degradation of black soil areas is a serious threat to national food security and ecological safety; nevertheless, the current lack of information on the location, size, and condition of black soil farmland productivity is a major obstacle to the development of strategies for the sustainable utilization of black soil resources. We synthesized remote sensing data and geospatial thematic data to construct a farmland productivity assessment indicator system to assess the productivity of black soil cropland at the regional scale. Furthermore, we conducted research on the spatial differentiation patterns and a spatial autocorrelation analysis of the assessment results. We found that farmland productivity within this region exhibited a decline pattern from south to north, with superior productivity in the east as opposed to the west, and the distribution follows a “spindle-shaped” pattern. Notably, the Songnen and Sanjiang typical black soil subregions centrally hosted about 46.17% of high-quality farmland and 53.51% of medium-quality farmland, while the Mondong typical black soil subregion in the west predominantly consisted of relatively low-quality farmland productivity. Additionally, farmland productivity displayed a significant positive spatial correlation and spatial clustering, with more pronounced fluctuations in the northeast–southwest direction. The developed indicator system for farmland productivity can illustrate the spatial differentiation and thereby offer a valuable reference for the sustainable management of farmland resources.
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(This article belongs to the Special Issue Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing)
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