SAR Images Processing and Analysis (2nd Edition)

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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