Exploiting the sparsity of edge information in synthetic aperture radar imagery for speckle reduction

摘要

Synthetic aperture radar (SAR) images are corrupted with speckle noise, which manifests as a multiplicative gamma noise in homogeneous regions, and reduces the contrast in imagery, making detection and classification using SAR images a difficult task. Many speckle reduction techniques aim to reduce this noise without including available prior knowledge about the speckle and the scene contents. In this investigation, we develop a new technique for speckle reduction which incorporates both the statistical model of speckle and the a priori knowledge about the sparsity of edges present in the scene. Using the proposed technique, we despeckle a synthetic image, a SAR image from the MSTAR1 data set and a SAR image from the Gotcha2 data set. Our results show that, with our method, we are able to visually improve the quality of SAR images. Various speckle reduction metrics are used to show quantitatively that our techniques reduce speckle in homogeneous areas beyond comparable methods, while maintaining edge and target intensity information.