NDLI: SAR image segmentation based on spatially adaptive weighted possibilistic c-means clustering

Content Provider	IEEE Xplore Digital Library
Author	Xiaolin Tian Shuiping Gou Licheng Jiao
Copyright Year	2007
Description	Author affiliation: Xidian Univ., Xian (Xiaolin Tian; Shuiping Gou; Licheng Jiao)
Abstract	Due to the influence of speckle in synthetic aperture radar (SAR) image, statistical dependencies among neighboring pixels should be considered in SAR image segmentation. The spatially adaptive weighted possibilistic c-means (SAW-PCM) clustering algorithm is proposed in which spatial information is introduced into PCM approach to directly adjust the membership. The relationship between the neighboring pixels is described through Markov random fields (MRF). To preserve detail information in SAR images, the directional neighborhood system set is established. The selection of neighborhood systems is based on similarity measurement (SM) between wavelet energies of comprehensive result of steerable wavelet transform. Among the different neighborhood alternatives, the one with the highest SM value is chosen to compute the weight value. The experimental results on real SAR images demonstrate the merit of the proposed method, especially in the preservation of details within a SAR image.
Starting Page	304
Ending Page	307
File Size	1697491
Page Count	4
File Format	PDF
ISBN	9781424414468
DOI	10.1109/ISPACS.2007.4445884
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Synthetic aperture radar Samarium Speckle Pixel Clustering algorithms Phase change materials Markov random fields Energy measurement Wavelet transforms fuzzy membership synthetic aperture radar (SAR) image segmentation possibilistic c-means (PCM) clustering steerable wavelet transform
Content Type	Text
Resource Type	Article

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