基于局部相似性测度的SAR图像多层分割

doi:10.3969/j.issn.1001-2400.2010.06.016

Abstract

Abstract:

Aiming at the high computational complexity of spectral clustering algorithms and their unsuitablity to synthetic aperture radar image segmentation, a novel SAR image multilevel segmentation based on local similarity measure is proposed which utilizes the equivalence of spectral clustering and weighted kernel k-means. First, the wavelet texture features of every pixel are extracted from the image and the sparse adjacent matrix among pixels is constructed by computing the local scale parameter of each pixel, and then multilevel merging, basic clustering and multilevel refining operations are used to cluster the image pixels, and finally the image segmentation result is obtained. Experimental results on artificial texture images and SAR images show that the proposed method can avoid the sensitivity of traditional spectral clustering to the scale parameters and obtain a better segmentation performance.

Key words: image segmentation, synthetic aperture radar (SAR) image, similarity measure, spectral clustering, weighted kernel k means

LIU Han-qiang;JIAO Li-cheng;ZHAO Feng. SAR image multilevel segmentation based on local similarity measure[J].J4, 2010, 37(6): 1071-1076.

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SAR image multilevel segmentation based on local similarity measure

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