Study on Coherent Speckle Noise Suppression in the SAR Images Based on Regional Division

Polar snowmelt detection is of great importance for the study of global climate change, and synthetic aperture radar (SAR) images have been widely used for polar snowmelt detection because of its ability to provide round-the-clock, all-weather snowmelt detection. However, conventional snowmelt detection algorithms based on the SAR images have images that are susceptible to interference from coherent speckle noise, which leads to the problems of false pixel and missed change detection. To solve the above-mentioned problems, this article proposed a coherent speckle noise suppression algorithm for the SAR images based on the measure of heterogeneity. That is, the SAR images are divided into homogeneous regions, edge regions, and isolated strong scattering regions by the measure of heterogeneity, and different construction algorithms are used for different regions, which was applied to the Larsen C ice shelf. The results showed that the construction algorithm in this article achieved better results in noise suppression, structure preservation and detail retention, and the comprehensive performance was better in the homogeneous regions and edge regions, which could reduce the false alarm rate and leakage rate, and provided algorithmic support for the study of polar snowmelt detection.