Advance in SAR Image Despeckling

Dear Colleagues,

The applications of synthetic aperture radar (SAR) imaging have penetrated many fields, such as environmental monitoring, global change, disaster monitoring surface surveillance, and automatic target recognition and classification. However, speckles inevitably occur in SAR images, which are caused by the coherent superposition of a large number of randomly distributed radar echoes and have the characteristic of multiplicative noise. The speckle pattern is inherent in SAR images, which seriously deteriorates the visual effect of SAR images, increases the difficulty of SAR image interpretation and processing, and greatly restricts the reliability and effectiveness of SAR image feature extraction, target tracking, and other interpretation processing technologies. In most SAR imaging applications, speckle filtering is usually the first problem to be addressed in image interpretation. To date, many speckle suppression methods have been proposed, including spatial-domain filtering, transform-domain filtering, and deep learning methods. Better speckle suppression processing usually consists of the smooth performance of speckle noise and the retention ability of edge details.

This Special Issue provides a chance for researchers to discuss the research progress and the advanced despeckling methods. With the theme of advances in SAR image despeckling, this Special Issue covers broad topics including but not limited to the following:

• Spatial-domain algorithms based on local statistics, e.g., an adaptive noise smoothing filter;
• Transform-domain algorithms, e.g., wavelet filter and anisotropic diffusion filter;
• Deep learning algorithms—deep learning algorithms are still in the early stage of research and need more improvement to become more general and stable;
• Recent advances in speckle suppression methods.

Prof. Dr. Qingjun Zhang
Prof. Dr. Zhenfang Li
Prof. Dr. Robert Wang
Prof. Dr. Jian Yang
Guest Editors

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