Superpixel Segmentation for Polarimetric SAR Imagery Using Local Iterative Clustering

Abstract

The simple linear iterative clustering (SLIC) algorithm shows good performance in superpixel generation for optical imagery. However, SLIC can perform poorly when there is too much noise in the image. To solve this problem, we have improved the cluster center initialization step and the postprocessing step, and then introduce the SLIC superpixel segmentation algorithm to the polarimetric synthetic aperture radar (PolSAR) image processing field. Experiments using AirSAR and ESAR L-band PolSAR data show that the improved SLIC algorithm can overcome the effect of speckle noise in PolSAR imagery, and it shows a better performance in detail preservation than the original SLIC algorithm and the normalized cuts superpixel segmentation algorithm.