Superpixel-based segmentation for multi-temporal PolSAR images

Abstract

Over-segmentation, or superpixel segmentation is widely applied in various image processing tasks such as image classification and region-based change detection. In polarimetric synthetic aperture radar (PolSAR) image processing, the complex Wishart distribution is commonly used in the modeling of homogeneous regions. In this paper, we introduce a segmentation method for multi-temporal PolSAR images based on the simple linear iterative clustering (SLIC) framework. We apply the Wishart distribution-based distance and modify the combination form for the SLIC distance measure such that the SLIC method can be adapted to the statistical characteristics of PolSAR imagery. Compared with existing PolSAR image superpixel methods, the proposed method has better time cost efficiency while maintaining the performance on segmentation results. Moreover, the proposed method is capable of jointly segmenting two or more multi-temporal PolSAR images, which is the basis for some further earth observation applications such as change detection and region-based data fusion tasks.