Hierarchical Unsupervised Segmentation of SAR Images via Super Pixel and Lossy Data Compression

This paper describes a method named hierarchical unsupervised segmentation using lossy data compression for synthetic aperture radar (SAR) images in which super pixels are used instead of pixels. In this paper, using a combination of several features such as edge, texture, and intensity, the super pixels are merged where the merging procedure is done by two steps. Since, many superpixel-based methods suffer from long run-times, the two step-proposed algorithm beside improving the accuracy, reduces the running-time. The first step is merging super pixels which there are not discriminative and clear edges between them. The merging of super pixels is done in the second step, if the length of data codes is minimized under a distinct distortion. The proposed algorithm is performed on SAR images and the simulation results demonstrate that the algorithm has a good accuracy and high processing speed.