TITLE

A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution

AUTHOR(S)
Huanxin Zou; Xianxiang Qin; Shilin Zhou; Kefeng Ji
PUB. DATE
July 2016
SOURCE
Sensors (14248220);Jul2016, Vol. 16 Issue 7, p1107
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
The simple linear iterative clustering (SLIC) method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR) images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed. This algorithm exploits the likelihood information of SAR image pixel clusters. Specifically, a local clustering scheme combining intensity similarity with spatial proximity is proposed. Additionally, for post-processing, a local edge-evolving scheme that combines spatial context and likelihood information is introduced as an alternative to the connected components algorithm. To estimate the likelihood information of SAR image clusters, we incorporated a generalized gamma distribution (GÃD). Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images.
ACCESSION #
120482468

 

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