Computer Science ›› 2015, Vol. 42 ›› Issue (Z6): 215-217.

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Local Nonlinear Distribution Feature and Extraction Algorithm for SAR Images

GUAN Tao and YU Hao-jie   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Spectral clustering is a current research focus and most algorithms are applied to image segmentation.With the capacity of finding low dimension space of spectral clustering,this paper analyzed the principle of feature representation of spectral clustering,proposed a new local nonlinear distribution feature extracted from sub-blocks of SAR images and used to describe the properties of sub-blocks.These feature vectors are rotationally invariant and they are obtained via several steps.First,the initial feature vectors are obtained via spectral clustering and then they are transformed by discrete Fourier transform.We used Nystrm approach to compute the eigenvalues of spectral clustering.In order to avoid weakening the difference of local characteristics in sub-graphs,we adopted Minkowski distance to compute simi-larity among sub-graphs.The efficiency of our features is validated by experiments.

Key words: Spectral clustering,Shift invariant feature,Image feature,SAR image,Image patch

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