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Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) sensors are widely used in remote sensing applications for their all-day and all-weather imaging ability. SAR signals can penetrate the atmosphere, clouds and rain, and even the ground surface and vegetation. Compared with optical data, SAR images have the following advantages for application in land monitoring: Firstly, they allow for periodical observation of the same area without the effects of bad weather conditions, which is of great value for applications such as change detection. Secondly, they contain rich polarization information. Different polarization combinations in polarimetric SAR (PolSAR) can obtain more scattering information about the interested ground objects. Thirdly, this technology may be used in interferometric measurements. Interferometric SAR (InSAR) technology can implement high-precision (up to millimeters) surface displacement measurement and height retrieval, and is therefore widely used in digital elevation model generation, volcanos and mine site monitoring, deformation detection and quantification, etc.

In recent years, a vast amount of research has been conducted for processing SAR images. To name several uses, polarimetric target decomposition decomposes the pixel-derived polarimetric SAR data into multiple components with physical characteristics. Further, they can be utilized in advanced InSAR, PSInSAR, and TomoSAR approaches for various displacement monitoring scenarios. Additionally, machine learning and deep learning methods have use in SAR image interpretation. This Special Issue aims to include the recent developments in processing methods and analysis tailored to SAR images. We look forward to original submissions related, but not necessarily restricted to:

Pre-processing of SAR images;
PolSAR image processing;
Advanced InSAR, DInSAR, PSInSAR, TomoSAR technologies;
SAR image time series processing;
Machine learning and deep learning methods for SAR images;
Inverse SAR imaging;
SAR image simulation;
Application of SAR images.

Dr. Qian Song
Dr. Xiao Wang
Dr. Feng Wang
Dr. Oleg Antropov
Guest Editors

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Related Special Issue

Published Papers (2 papers)

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Research

21 pages, 13557 KiB

Open AccessArticle

An Adaptive Polarimetric Target Decomposition Algorithm Based on the Anisotropic Degree

by Pingping Huang, Baoyu Li, Xiujuan Li, Weixian Tan, Wei Xu and Yuejuan Chen

Remote Sens. 2024, 16(6), 1015; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16061015 - 13 Mar 2024

Viewed by 564

Abstract

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data. Full article

(This article belongs to the Special Issue SAR Images Processing and Analysis (2nd Edition))

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Graphical abstract

25 pages, 8820 KiB

Open AccessArticle

YOLOv7oSAR: A Lightweight High-Precision Ship Detection Model for SAR Images Based on the YOLOv7 Algorithm

by Yilin Liu, Yong Ma, Fu Chen, Erping Shang, Wutao Yao, Shuyan Zhang and Jin Yang

Remote Sens. 2024, 16(5), 913; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050913 - 05 Mar 2024

Viewed by 853

Abstract

Researchers have explored various methods to fully exploit the all-weather characteristics of Synthetic aperture radar (SAR) images to achieve high-precision, real-time, computationally efficient, and easily deployable ship target detection models. These methods include Constant False Alarm Rate (CFAR) algorithms and deep learning approaches such as RCNN, YOLO, and SSD, among others. While these methods outperform traditional algorithms in SAR ship detection, challenges still exist in handling the arbitrary ship distributions and small target features in SAR remote sensing images. Existing models are complex, with a large number of parameters, hindering effective deployment. This paper introduces a YOLOv7 oriented bounding box SAR ship detection model (YOLOv7oSAR). The model employs a rotation box detection mechanism, uses the KLD loss function to enhance accuracy, and introduces a Bi-former attention mechanism to improve small target detection. By redesigning the network’s width and depth and incorporating a lightweight P-ELAN structure, the model effectively reduces its size and computational requirements. The proposed model achieves high-precision detection results on the public RSDD dataset (94.8% offshore, 66.6% nearshore), and its generalization ability is validated on a custom dataset (94.2% overall detection accuracy). Full article

(This article belongs to the Special Issue SAR Images Processing and Analysis (2nd Edition))

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