SUPERPIXEL SEGMENTATION OF POLARIMETRIC SAR IMAGE USING GENERALIZED MEAN SHIFT

被引：1

作者：

Lang, Fengkai ^{[1
]}

Yang, Jie ^{[2
]}

Wu, Lixin ^{[1
]}

Xu, Jinyan ^{[3
]}

机构：

[1] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou, Peoples R China

[2] Wuhan Univ, LIESMARS, Wuhan, Peoples R China

[3] State Ocean Adm, Isl Res Ctr, Pingtan, Peoples R China

来源：

2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年

关键词：

synthetic aperture radar (SAR); polarimetric SAR (PolSAR); superpixel; segmentation; mean shift; CLASSIFICATION; AREAS;

D O I：

10.1109/IGARSS.2016.7730653

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The mean shift algorithm shows a good performance in optical image segmentation. However, conventional mean shift algorithm performs poorly if it is used directly to synthetic aperture radar (SAR) image due to the large dynamic range and strong speckle noise. Recently, a generalized mean shift (GMS) algorithm with an adaptive variable asymmetric bandwidth was proposed for polarimetric SAR (PolSAR) image filtering. In this paper, it is further developed and extended for PolSAR image segmentation. The proposed algorithm can be used for PolSAR image superpixel segmentation directly without any preprocessing steps. Experiments using AirSAR and ESAR L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm.

引用

页码：6324 / 6327

页数：4

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