Volume 4 Issue 1
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Article Navigation >  Journal of Radars  > 2015  >  4(1): 93-98
Hua Wen-qiang, Wang Shuang, Hou Biao. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart[J]. Journal of Radars, 2015, 4(1): 93-98. doi: 10.12000/JR14138
Citation: Hua Wen-qiang, Wang Shuang, Hou Biao. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart[J]. Journal of Radars, 2015, 4(1): 93-98. doi: 10.12000/JR14138
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Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

DOI: 10.12000/JR14138
  • 1.

    (Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi’an 710071, China)

  • 2.

    (International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an 710071, China)

  • Received Date: 2014-11-20
  • Rev Recd Date: 2015-02-28
  • Publish Date: 2015-02-28
    Abstract
  • In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR) images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM). Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

     

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    • References(17)
  • [1]
    Kersten P R, Lee J S, and Ainsworth 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.
    [2]
    Wang S, Liu K, Pei J J, et al.. Unsupervised classification of fully polarimetric SAR images based on scattering power entropy and copolarized ratio[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 622-626.
    [3]
    Shang F and Hirose A. Use of poincare sphere parameters for fast supervised PolSAR land classification[C]. IEEE Geoscience and Remote Sensing Symposium, Melbourne, Australia, 2013: 3175-3178.
    [4]
    Shi L, Zhang L F, and Yang J. Supervised graph embedding for Polarimetric SAR image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(2): 216-220.
    [5]
    Hady M and Schwenker F. Co-training by committee: a new semi-supervised learning framework[C]. IEEE International Conference on Data Mining Workshops, 2008: 563-572.
    [6]
    Hansch R and Hellwich O. Semi-supervised learning for classification of polarimetric SAR-data[C]. IEEE Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 2009: 987-990.
    [7]
    Lee J S, Grunes M R, and Famil L F. Unsupervised terrain classification preserving polarimetric scattering characteristics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 722-731.
    [8]
    He Y and Cheng J. Classification based on Four-component decomposition and SVM for PolSAR images[C]. IEEE International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), Xiamen, China, 2012: 635-637.
    [9]
    Blum A and Mitchell T. Combining labeled and unlabeled data with co-training[C]. Proceedings of the 11th Annual Conference on Computational Learning Theory, Wisconsin, USA, 1998: 92-100.
    [10]
    Cloude S R and Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
    [11]
    Cloude S R and Pottier E. An entropy based classification scheme for land application of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 68-78.
    [12]
    Krogager E. New decomposition of the radar target scattering matrix[J]. Electronics Letters, 1990, 26(18): 1525-1527.
    [13]
    Huang J, Shirabad J S, Matwin S, et al.. Improving cotraining with agreement-based sampling[C]. 7th International Conference, RSCTC, Warsaw, Poland, 2010: 197-206.
    [14]
    边肇祺, 张学工, 等. 模式识别[M]. 北京: 清华大学出版社, 2000: 136-140. Bian Z Q, Zhang X G, et al.. Pattern Recognition[M]. Beijing: Tsinghua University Press, 2000: 136-140.
    [15]
    Zanchettin C, Bezerra B L, and Azevedo W A. A KNN-SVM hybrid model for cursive handwriting recognition[C]. WCCI IEEE World Congress on Computational Intelligence, Brisbane, Australia, 2012, 6: 10-15.
    [16]
    Lee J S, Grunes M R, and Kwok R. Classification of multilook polarimetric SAR imagery based on complex Wishart distribution[J]. International Journal of Remote Sensing, 1994, 15(11): 2299-2311.
    [17]
    Lee J S, Grunes M R, and Grandi G. Polarimetric SAR speckle filtering and its implication for classification[J]. IEEE Transcations on Geoscience and Remote Sensing, 1999, 37(5): 2363-2373.
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