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Sensors 2016, 16(7), 1107; doi:10.3390/s16071107

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

1
College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
2
School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 26 March 2016 / Revised: 7 July 2016 / Accepted: 13 July 2016 / Published: 18 July 2016
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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 (GГD). Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images. View Full-Text
Keywords: superpixel; simple linear iterative clustering; likelihood; synthetic aperture radar; generalized gamma distribution; edge evolving superpixel; simple linear iterative clustering; likelihood; synthetic aperture radar; generalized gamma distribution; edge evolving
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Zou, H.; Qin, X.; Zhou, S.; Ji, K. A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution. Sensors 2016, 16, 1107.

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