Unsupervised Classification of High-Resolution SAR Images Using Multilayer Level Set Method

Synthetic aperture radar (SAR) image classification is a challenging subject due to the strong speckle noise present in SAR image processing. This paper is devoted to the unsupervised classification of single-band single-polarized synthetic aperture radar (SAR) images using multilayer level set approach. The principal concept is that we employ the gamma model to define the multilayer level set energy functional. The experiments conducted on both synthetic and real SAR images show that the proposed algorithm obtains improved experimental results in terms of both accuracy and efficiency.