期刊
REMOTE SENSING
卷 10, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/rs10101592
关键词
synthetic aperture radar (SAR); polarimetric SAR (PolSAR); superpixel; segmentation; mean shift
类别
资金
- National Natural Science Foundation for Young Scientists of China [61601465]
- Natural Science Foundation of Jiangsu Province [BK20160244, BK20150189]
- Applied Basic Research Project of Sichuan Province [2018JY0318]
The mean shift algorithm has been shown to perform well in optical image segmentation. However, the conventional mean shift algorithm performs poorly if it is directly used with Synthetic Aperture Radar (SAR) images due to the large dynamic range and strong speckle noise. Recently, the Generalized Mean Shift (GMS) algorithm with an adaptive variable asymmetric bandwidth has been proposed for Polarimetric SAR (PolSAR) image filtering. In this paper, the GMS algorithm is further developed for PolSAR image segmentation. A new merging predicate that is defined in the joint spatial-range domain is derived based on the GMS algorithm. A pre-sorting strategy and a post-processing step are also introduced into the GMS segmentation algorithm. The proposed algorithm can be directly used for PolSAR image superpixel segmentation without any pre-processing steps. Experiments using Airborne SAR (AirSAR) and Experimental SAR (ESAR) L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm. The parameter settings, stability, quality, and efficiency of the GMS algorithm are also discussed at the end of this paper.
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