NDLI: Texture-based Segmentation of High Resolution SAR Images Using Contourlet Transform and Mean Shift

Content Provider	IEEE Xplore Digital Library
Author	Li Yingqi He Mingyi
Copyright Year	2006
Description	Author affiliation: Coll. of Electron. Eng., Northwestern Polytech. Univ., Xi'an (Li Yingqi; He Mingyi)
Abstract	This paper presents an unsupervised texture-based segmentation algorithm which uses reduced contourlet transform sub-bands and mean shift clustering, to analysis the texture information of high resolution SAR images. One step and criteria is proposed to reduce the sub-bands and other's is presented to decrease the number of dimension of the feature space. The mean shift clustering method is used to obtain the number of texture regions and the centre of the label class. Group the pixels into corresponding texture region by their simple distance to the class centre pixel. Experiments on a mixture of Brodatz texture and SAR images show the proposed algorithm of using contourlet transform and mean shift clustering gives satisfactory results.
Starting Page	201
Ending Page	206
File Size	2475794
Page Count	6
File Format	PDF
ISBN	1424405289
DOI	10.1109/ICIA.2006.305994
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	unsupervised image segmentation Image resolution mean shift SAR texture Discrete wavelet transforms Image texture analysis Information analysis Image segmentation Layout Vegetation mapping Clustering algorithms Remote monitoring Pixel feature selection contourlet transform
Content Type	Text
Resource Type	Article

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