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Radar Remote Sensing and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (27 April 2024) | Viewed by 13784

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Special Issue Editor

Dr. Shunjun Wei

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Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar system and sensing; SAR imaging and application; object identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar sensors are pervasive in remote sensing and related applications. Today, the available radar systems and the corresponding algorithms are still not perfect, but compared to twenty years ago, they are more robust, sophisticated, and user-friendly, and the radar data are of higher quality. Many radar platforms and algorithms are still for specialized tasks/applications only. Therefore, original and innovative methodological contributions to address the key challenge of radar system design and algorithmic data processing are highly required. As for radar remote sensing data, a key challenge is that systematic acquisitions are still insufficient, despite the availability of a few consolidated products. This hampers applications. Positive attempts to obtain application-oriented radar data have been carried out by many academies, research institutes, and space agencies, but the related research is still quite demanding. Otherwise, deep learning techniques bring the biggest breakthroughs in the machine learning field. In radar remote sensing, deep learning leverages the nature of huge remote sensing data and provides an end-to-end fashion where powerful adaptive feature representations can be automatically learned from the raw data. In response, the purpose of this Special Issue is to provide a platform for the discussion of the major challenges, latest developments, and recent advances in radar remote sensing and SAR-related fields.

Potential topics include, but are not limited to, the following points:

Advanced radar sensing systems, algorithms, and technologies;
Synthetic aperture radar (SAR) technologies and applications;
Millimetre wave radar, Terahertz radar, and LIDAR techniques;
Distributed radar, MIMO radar imaging, and applications;
Advanced data acquisition and signal processing for radar applications;
Novel designing of radar waveform, antenna, and systems;
Simultaneous localization and mapping (SLAM) with radar sensors;
Jamming and anti-jamming techniques for radar systems and applications;
Image processing and multi-sensor fusion in remote sensing scenarios;
Image interpretation and segmentation, object detection and recognition in radar remote sensing;
Artificial intelligence and machine learning-based approaches to radar sensing;
Sensing applications with radar in earth observation, ocean, agriculture, city, and so on;
Advanced data visualization techniques of radar sensing;
Object reconstruction from multidimensional radar point clouds.

Dr. Shunjun Wei
Guest Editor

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Keywords

radar sensing applications
remote sensing
radar imaging and detection
advanced radar systems
SLAM
jamming and anti-jamming
multi-sensor fusion
artificial intelligence

Published Papers (10 papers)

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Research

18 pages, 9869 KiB

Open AccessArticle

A Cloud Detection Method for Vertically Pointing Millimeter-Wavelength Cloud Radar

by Hai Lin, Jie Wang and Junxiang Ge

Sensors 2023, 23(21), 8891; https://doi.org/10.3390/s23218891 - 01 Nov 2023

Viewed by 707

Abstract

A new method using three dimensions of cloud continuity, including range dimension, Doppler dimension, and time dimension, is proposed to discriminate cloud from noise and detect more weak cloud signals in vertically pointing millimeter-wave cloud radar observations by fully utilizing the spatiotemporal continuum of clouds. A modified noise level estimation method based on the Hildebrand and Sekhon algorithm is used for more accurate noise level estimation, which is critical for weak signals. The detection method consists of three steps. The first two steps are performed at the Doppler power spectrum stage, while the third step is performed at the base data stage. In the first step, a new adaptive spatial filter combined with the Kuwaraha filter and the Gaussian filter, using the ratio of mean to standard deviation as the adaptive parameter, is applied to initially mask the potential cloud signals to improve the detection performance at the boundary of cloud and noise. Simulations of boundary cases were performed to compare our adaptive filter and normal Gaussian filters. Box filters are used in steps two and three to remove the remaining noise. We applied our method to cloud radar observations with TJ-II cloud radar at the Nanjing University of Information Science & Technology. The results showed that our method can detect more weak cloud signals than the usual methods, which are performed only at the Doppler power spectrum stage or the base data stage. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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13 pages, 2612 KiB

Open AccessArticle

Tunable Optical Frequency Comb Generated Using Periodic Windows in a Laser and Its Application for Distance Measurement

by Zhuqiu Chen, Can Fang, Yuxi Ruan, Yanguang Yu, Qinghua Guo, Jun Tong and Jiangtao Xi

Sensors 2023, 23(21), 8872; https://doi.org/10.3390/s23218872 - 31 Oct 2023

Viewed by 1267

Abstract

A novel method for the generation of an optical frequency comb (OFC) is presented. The proposed approach uses a laser diode with optical feedback and operating at a specific nonlinear dynamic state named periodic window. In this case, the laser spectrum exhibits a feature with a series of discrete, equally spaced frequency components, and the repetition rate can be flexibly adjusted by varying the system parameters (e.g., external cavity length), which can provide many potential applications. As an application example, a dual-OFC system for distance measurement is presented. The results demonstrate the system’s ability to achieve target distance detection, underscoring its potential for real-world applications in this field. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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26 pages, 11062 KiB

Open AccessArticle

Comparing NISAR (Using Sentinel-1), USDA/NASS CDL, and Ground Truth Crop/Non-Crop Areas in an Urban Agricultural Region

by Simon Kraatz, Brian T. Lamb, W. Dean Hively, Jyoti S. Jennewein, Feng Gao, Michael H. Cosh and Paul Siqueira

Sensors 2023, 23(20), 8595; https://doi.org/10.3390/s23208595 - 20 Oct 2023

Viewed by 1173

Abstract

A general limitation in assessing the accuracy of land cover mapping is the availability of ground truth data. At sites where ground truth is not available, potentially inaccurate proxy datasets are used for sub-field-scale resolution investigations at large spatial scales, i.e., in the Contiguous United States. The USDA/NASS Cropland Data Layer (CDL) is a popular agricultural land cover dataset due to its high accuracy (>80%), resolution (30 m), and inclusions of many land cover and crop types. However, because the CDL is derived from satellite imagery and has resulting uncertainties, comparisons to available in situ data are necessary for verifying classification performance. This study compares the cropland mapping accuracies (crop/non-crop) of an optical approach (CDL) and the radar-based crop area (CA) approach used for the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) L- and S-band mission but using Sentinel-1 C-band data. CDL and CA performance are compared to ground truth data that includes 54 agricultural production and research fields located at USDA’s Beltsville Agricultural Research Center (BARC) in Maryland, USA. We also evaluate non-crop mapping accuracy using twenty-six built-up and thirteen forest sites at BARC. The results show that the CDL and CA have a good pixel-wise agreement with one another (87%). However, the CA is notably more accurate compared to ground truth data than the CDL. The 2017–2021 mean accuracies for the CDL and CA, respectively, are 77% and 96% for crop, 100% and 94% for built-up, and 100% and 100% for forest, yielding an overall accuracy of 86% for the CDL and 96% for CA. This difference mainly stems from the CDL under-detecting crop cover at BARC, especially in 2017 and 2018. We also note that annual accuracy levels varied less for the CA (91–98%) than for the CDL (79–93%). This study demonstrates that a computationally inexpensive radar-based cropland mapping approach can also give accurate results over complex landscapes with accuracies similar to or better than optical approaches. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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22 pages, 5181 KiB

Open AccessArticle

Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East

by Konstantin Dubrovin, Alexey Stepanov and Andrey Verkhoturov

Sensors 2023, 23(18), 7902; https://doi.org/10.3390/s23187902 - 15 Sep 2023

Cited by 1 | Viewed by 897

Abstract

Crop identification is one of the most important tasks in digital farming. The use of remote sensing data makes it possible to clarify the boundaries of fields and identify fallow land. This study considered the possibility of using the seasonal variation in the Dual-polarization Radar Vegetation Index (DpRVI), which was calculated based on data acquired by the Sentinel-1B satellite between May and October 2021, as the main characteristic. Radar images of the Khabarovskiy District of the Khabarovsk Territory, as well as those of the Arkharinskiy, Ivanovskiy, and Oktyabrskiy districts in the Amur Region (Russian Far East), were obtained and processed. The identifiable classes were soybean and oat crops, as well as fallow land. Classification was carried out using the Support Vector Machines, Quadratic Discriminant Analysis (QDA), and Random Forest (RF) algorithms. The training (848 ha) and test (364 ha) samples were located in Khabarovskiy District. The best overall accuracy on the test set (82.0%) was achieved using RF. Classification accuracy at the field level was 79%. When using the QDA classifier on cropland in the Amur Region (2324 ha), the overall classification accuracy was 83.1% (F1 was 0.86 for soybean, 0.84 for fallow, and 0.79 for oat). Application of the Radar Vegetation Index (RVI) and VV/VH ratio enabled an overall classification accuracy in the Amur region of 74.9% and 74.6%, respectively. Thus, using DpRVI allowed us to achieve greater performance compared to other SAR data, and it can be used to identify crops in the south of the Far East and serve as the basis for the automatic classification of cropland. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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17 pages, 11463 KiB

Open AccessArticle

Sar Ship Detection Based on Convnext with Multi-Pooling Channel Attention and Feature Intensification Pyramid Network

by Fanming Wei and Xiao Wang

Sensors 2023, 23(17), 7641; https://doi.org/10.3390/s23177641 - 03 Sep 2023

Viewed by 987

Abstract

The advancements in ship detection technology using convolutional neural networks (CNNs) regarding synthetic aperture radar (SAR) images have been significant. Yet, there are still some limitations in the existing detection algorithms. First, the backbones cannot generate high-quality multiscale feature maps. Second, there is a lack of suitable attention mechanisms to suppress false alarms. Third, the current feature intensification algorithms are unable to effectively enhance the shallow feature’s semantic information, which hinders the detection of small ships. Fourth, top-level feature maps have rich semantic information; however, as a result of the reduction of channels, the semantic information is weakened. These four problems lead to poor performance in SAR ship detection and recognition. To address the mentioned issues, we put forward a new approach that has the following characteristics. First, we use Convnext as the backbone to generate high-quality multiscale feature maps. Second, to suppress false alarms, the multi-pooling channel attention (MPCA) is designed to generate a corresponding weight for each channel, suppressing redundant feature maps, and further optimizing the feature maps generated by Convnext. Third, a feature intensification pyramid network (FIPN) is specifically designed to intensify the feature maps, especially the shallow feature maps. Fourth, a top-level feature intensification (TLFI) is also proposed to compensate for semantic information loss within the top-level feature maps by utilizing semantic information from different spaces. The experimental dataset employed is the SAR Ship Detection Dataset (SSDD), and the experimental findings display that our approach exhibits superiority compared to other advanced approaches. The overall Average Precision (AP) reaches up to 95.6% on the SSDD, which improves the accuracy by at least 1.7% compared to the current excellent methods. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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19 pages, 53132 KiB

Open AccessArticle

Utilising Sentinel-1’s Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter

by Claudio Navacchi, Senmao Cao, Bernhard Bauer-Marschallinger, Paul Snoeij, David Small and Wolfgang Wagner

Sensors 2023, 23(13), 6072; https://doi.org/10.3390/s23136072 - 01 Jul 2023

Cited by 3 | Viewed by 1033

Abstract

Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite’s high orbital stability. By propagating Sentinel-1’s orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10–50. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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18 pages, 103114 KiB

Open AccessArticle

An Enhanced Offset Tracking Method: Providing Auxiliary Information for DInSAR Phase Filtering in Urban Areas

by Qingyu Liu, Xiaoqi Lv, Pingping Huang and Wei Xu

Sensors 2023, 23(8), 3802; https://doi.org/10.3390/s23083802 - 07 Apr 2023

Viewed by 1019

Abstract

In the application of synthetic aperture radar differential interferometry in urban environments, it is easy to regard the phase change in the deformation band of buildings under construction as noise that requires filtering. This introduces an error into the surrounding area while over-filtering, resulting in an error in the magnitude of the deformation measurement results for the entire region and the loss of deformation details in the surrounding area. Based on the traditional DInSAR workflow, this study added a deformation magnitude identification step, determined the deformation magnitude by using enhanced offset tracking technology, supplemented the filtering quality map and removed the construction areas that affect the interferometry in the filtering stage. The enhanced offset tracking technique adjusted the ratio of contrast saliency and coherence via the contrast consistency peak in the radar intensity image, which was used as the basis for adjusting the adaptive window size. The method proposed in this paper was evaluated in an experiment on a stable region using simulated data and in an experiment on a large deformation region using Sentinel-1 data. The experimental results show that the enhanced method has a better anti-noise ability than the traditional method, and the accuracy rate is improved by about 12%. The supplemented quality map can effectively remove the large deformation area to prevent over-filtering while ensuring the filtering quality, and it can achieve better filtering results. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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16 pages, 21577 KiB

Open AccessArticle

Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR

by Huibao Huang, Shujun Ju, Wei Duan, Dejun Jiang, Zhiliang Gao and Heng Liu

Sensors 2023, 23(7), 3383; https://doi.org/10.3390/s23073383 - 23 Mar 2023

Cited by 6 | Viewed by 1276

Abstract

The Dadu River travels in the mountainous areas of southwestern China, one of regions with the most hazards that has long suffered from frequent geohazards. The early identification of landslides in this region is urgently needed, especially after the recent Luding earthquake (MS 6.8). While conventional ground-based monitoring techniques are limited by the complex terrain conditions in these alpine valley regions, space interferometric synthetic aperture radar (InSAR) provides an incomparable advantage in obtaining surface deformation with high precision and over a wide area, which is very useful for long-term and slow geohazard monitoring. In this study, more than 500 Sentinel-1 SAR images with four frames acquired during 2017~2022 were collected to detect the hidden landslide regions from the Jinchuan to Ebian Section along the Dadu River, based on joint-scatterer InSAR (JS-InSAR) and small baseline subset (SBAS) techniques. The results showed that our method could be successfully applied for landslide monitoring in complex mountainous regions. Furthermore, 143 potential landslide regions spreading over an 800 km area along the Dadu River were extracted by integrating the deformation measurements and optical images. Our study can provide a reference for large-scale geological hazard surveys in mountainous areas, and the InSAR technique will be encouraged for the local government in future long-term monitoring applications in the Dadu River Basin. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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14 pages, 4966 KiB

Open AccessArticle

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

by Arturo Villarroya-Carpio and Juan M. Lopez-Sanchez

Sensors 2023, 23(4), 1833; https://doi.org/10.3390/s23041833 - 07 Feb 2023

Cited by 6 | Viewed by 1686

Abstract

Interferometric coherence from SAR data is a tool used in a variety of Earth observation applications. In the context of crop monitoring, vegetation indices are commonly used to describe crop dynamics. The most frequently used vegetation indices based on radar data are constructed using the backscattered intensity at different polarimetric channels. As coherence is sensitive to the changes in the scene caused by vegetation and its evolution, it may potentially be used as an alternative tool in this context. The objective of this work is to evaluate the potential of using Sentinel-1 interferometric coherence for this purpose. The study area is an agricultural region in Sevilla, Spain, mainly covered by 18 different crops. Time series of different backscatter-based radar vegetation indices and the coherence amplitude for both VV and VH channels from Sentinel-1 were compared to the NDVI derived from Sentinel-2 imagery for a 5-year period, from 2017 to 2021. The correlations between the series were studied both during and outside the growing season of the crops. Additionally, the use of the ratio of the two coherences measured at both polarimetric channels was explored. The results show that the coherence is generally well correlated with the NDVI across all seasons. The ratio between coherences at each channel is a potential alternative to the separate channels when the analysis is not restricted to the growing season of the crop, as its year-long temporal evolution more closely resembles that of the NDVI. Coherence and backscatter can be used as complementary sources of information, as backscatter-based indices describe the evolution of certain crops better than coherence. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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24 pages, 12378 KiB

Open AccessArticle

Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China

by Bo Xiao, Junsan Zhao, Dongsheng Li, Zhenfeng Zhao, Dingyi Zhou, Wenfei Xi and Yangyang Li

Sensors 2022, 22(20), 8041; https://doi.org/10.3390/s22208041 - 21 Oct 2022

Cited by 7 | Viewed by 1917

Abstract

In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments. Full article

(This article belongs to the Special Issue Radar Remote Sensing and Applications)

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