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 CSTR: 32380.14.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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    • References(18)
  • [1]
    Yang W, Zhong N, Yang X, et al.. Riemannian sparse coding for classification of PolSAR images[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016: 5698-5701.
    [2]
    田维, 徐旭, 卞小林, 等.环境一号C卫星SAR图像典型环境遥感应用初探[J].雷达学报, 2014, 3(3): 339-351. http://radars.ie.ac.cn/CN/abstract/abstract147.shtml

    Tian Wei, Xu Xu, Bian Xiao-lin, et al.. Application of environment remote sensing by HJ-1C SAR imagery[J]. Journal of Radars, 2014, 3(3): 339-351. http://radars.ie.ac.cn/CN/abstract/abstract147.shtml
    [3]
    Lee J S, Grunes M R, Ainworth T L, et al.. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1998, 4: 2178-2180.
    [4]
    Ferro-Famil L, Pottier E, and Lee J S. Unsupervised classification of multi-frequency and fully polarimetric SAR images based on the H/A/Alpha Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2332-2342. doi: 10.1109/36.964969
    [5]
    Ersahin K, Cumming I G, and Yedlin M J. Classification of Polarimetric SAR data using spectral graph partitioning[C]. IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), Denver, USA, 1999: 1756-1759.
    [6]
    Kersten P R, Lee J S, and Ainworth T L. Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 519-527. doi: 10.1109/TGRS.2004.842108
    [7]
    Song H, Yang W, Bai Y, et al.. Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints[J]. International Journal of Remote Sensing, 2015, 36(11): 2816-2830. doi: 10.1080/01431161.2015.1043759
    [8]
    Wang Y, Han C, and Tupin F. PolSAR data segmentation by combining tensor space cluster analysis and Markovian framework[J].IEEE Geoscience and Remote Sensing Letters, 2010, 7(1): 210-214. doi: 10.1109/LGRS.2009.2031660
    [9]
    Rodriguez A and Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344: 1492-1496. doi: 10.1126/science.1242072
    [10]
    Tran T N, Wehrens R, Hoekman D H, et al.. Initialization of Markovian random field clustering of large remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1912-1919. doi: 10.1109/TGRS.2005.848427
    [11]
    Cao F, Hong W, Wu Y, et al.. An unsupervised segmentation with an adaptive number of clusters using the SPAN/H/a/A space and the complex Wishart clustering for fully Polarimetric SAR data analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(11): 3454-3467. doi: 10.1109/TGRS.2007.907601
    [12]
    Liu B, Hu H, Wang H, et al.. Superpixel-based classification with an adaptive number of classes for polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 907-924. doi: 10.1109/TGRS.2012.2203358
    [13]
    Achanta R, Shaji A, Smith K, et al.. SLIC superpixel compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282. doi: 10.1109/TPAMI.2012.120
    [14]
    Yang W, Yang X L, Yan T H, et al.. Region-based change detection for polarimetric SAR images using wishart mixture models[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(11): 6746-6756. doi: 10.1109/TGRS.2016.2590145
    [15]
    Nielsen F. K-MLE: A fast algorithm for learning statistical mixture models[C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, 2012: 869-872.
    [16]
    Nielsen F. Closed-form information-theoretic divergences for statistical mixtures[C]. International Conference on Pattern Recognition, Tsukuba, 2012: 1723-1726.
    [17]
    谢娟英, 高红超, 谢维信. K近邻优化的密度峰值快速搜索聚类算法[J].中国科学:信息科学, 2016, 46(2): 258-280. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=pzkx201602008&dbname=CJFD&dbcode=CJFQ

    Xie J Y, Gao H C, and Xie W X. K-nearnestneighbors optimized clustering algorithm by fastsearch and finding the density peaks of a dataset[J]. Scientia Sinica Informationis, 2016, 46(2): 258-280. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=pzkx201602008&dbname=CJFD&dbcode=CJFQ
    [18]
    Cherian A, Morellas V, and Papanikolopoulos N. Bayesian nonparametric clustering for positive definite matrices[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(5): 862-874. doi: 10.1109/TPAMI.2015.2456903
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