SAR remote sensing image segmentation based on feature enhancement

Synthetic aperture radar (SAR) images are crucial in remote sensing due to their ability to capture high-quality images regardless of environmental conditions. Though it has been studied for years, the following aspects still limit its further improvement. (1) Due to the unique imaging mechanism of SAR images, the influence of speckle noise cannot be avoided. (2) High-resolution SAR remote sensing images contain complex surface features, and the intersection of multiple targets makes boundary information unclear. To address these problems, we propose a SAR remote sensing image segmentation method based on feature enhancement. Specifically, we propose utilizing wavelet transform on the original SAR remote sensing image along with an encoder–decoder network to learn the structural features. This approach enhances the feature expression and mitigates the impact of speckle noise. Secondly, we design a post-processing refinement module that consists of a small cascaded encoder–decoder. This module refines the segmentation results, making the boundary information clearer. Finally, to further enhance the segmentation results, we incorporate a self-distillation module into the encoder. This enhances hierarchical interaction in the encoder, enabling better learning of semantic information by the shallow layer for segmentation. Two SAR image segmentation datasets demonstrate the effectiveness of the proposed method.