Volume 6 Issue 5
Oct.  2017
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Article Navigation >  Journal of Radars  > 2017  >  6(5): 533-540
Zhong Neng, Yang Wen, Yang Xiangli, Guo Wei. Unsupervised Classification for Polarimetric Synthetic Aperture Radar Images Based on Wishart Mixture Models[J]. Journal of Radars, 2017, 6(5): 533-540. doi: 10.12000/JR16133
Citation: Zhong Neng, Yang Wen, Yang Xiangli, Guo Wei. Unsupervised Classification for Polarimetric Synthetic Aperture Radar Images Based on Wishart Mixture Models[J]. Journal of Radars, 2017, 6(5): 533-540. doi: 10.12000/JR16133
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Unsupervised Classification for Polarimetric Synthetic Aperture Radar Images Based on Wishart Mixture Models

doi: 10.12000/JR16133

  • School of Electronic Information, Wuhan University, Wuhan 430072, China

Funds:

The National Natural Science Foundation of China 61331016

The National Natural Science Foundation of China 61271401

  • Received Date: 2016-11-30
  • Rev Recd Date: 2017-01-16
    • Available Online: 2017-02-17
  • Publish Date: 2017-10-28
    Abstract
  • Unsupervised classification is a significant step inthe automated interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, determining the number of clusters in this process is still a challenging problem. To this end, we propose a region-based unsupervised classification method for PolSAR images by introducing Wishart mixture models and a Density Peaks Clustering (DPC) algorithm. More precisely, the Simple Linear Iterative Clustering (SLIC) algorithm is first used to segment the PolSAR image into superpixels. Subsequently, the Wishart mixture models are adopted to model each superpixel, and the pairwise distances between different superpixels are measured by Cauchy-Schwarz divergence. Finally, the unsupervised classification result of the PolSAR image is obtained via clustering by fast search and find of density peaks. The experimental results obtained from different PolSAR images demonstrate that the proposed method is effective.

     

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