YUAN Zhengwu, CHEN Ran, CHEN Cuiping, LUO Xiaobo, LIU Minghao. Object-Based Classification Method for PolSAR Images with Improved Scattering Powers and Contextual Features[J]. Chinese Journal of Electronics, 2017, 26(4): 803-809. doi: 10.1049/cje.2017.03.017
Citation: YUAN Zhengwu, CHEN Ran, CHEN Cuiping, LUO Xiaobo, LIU Minghao. Object-Based Classification Method for PolSAR Images with Improved Scattering Powers and Contextual Features[J]. Chinese Journal of Electronics, 2017, 26(4): 803-809. doi: 10.1049/cje.2017.03.017

Object-Based Classification Method for PolSAR Images with Improved Scattering Powers and Contextual Features

doi: 10.1049/cje.2017.03.017
Funds:  This work is supported by the National Natural Science Foundation of China (No.41301384), and Scientific and Technological Research Program of Chongqing Municipal Education Commission (No.KJ120517, No.KJ1400420).
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  • Corresponding author: LUO Xiaobo (corresponding author) is an associate professor of Chongqing University of Post and Telecommunications, China. His current research interests mainly include thermal infrared remote sensing and its application in urban thermal environment and ecological environment monitoring and evaluating. (Email: luoxb@cqupt.edu.cn)
  • Received Date: 2015-10-22
  • Rev Recd Date: 2016-07-13
  • Publish Date: 2017-07-10
  • This paper proposes a new object-based classification method for Polarimetric synthetic aperture radar (PolSAR) images, which considers scattering powers from an improved model-based polarimetric decomposition approach, as well as the spatial and textural features.With the decomposition, the scattering ambiguities between oriented buildings and vegetation are reduced. Furthermore, various contextual features are extracted from the object and incorporated into the K-nearest neighbors (k-NN) based classification. To reduce the feature redundancy, a new Supervised locally linear embedding (S-LLE) dimensionality reduction method is introduced to map the high dimensional polarimetric signatures into the most compact low-dimensional structure for classification. Experimental results with Airborne synthetic aperture rada (AIRSAR) C-band PolSAR image demonstrate the superior performance to other methods.
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  • B. Liu, H.Wang, Q. Yu, et al., “A novel ship detection approach for polarimetric SAR images based on a foreground/background separation framework”, Chinese Journal of Electronics, Vol.22, No.3, pp.641-647, 2013.
    S.W. Chen, Y.Z. Li and X.S. Wang, “Modeling and interpretation of scattering mechanisms in polarimetric synthetic aperture radar: advances and perspectives”, IEEE Signal Processing Magazine, Vol.31, No.4, pp.79-89, 2014.
    L. Zhang, B. Zou, J. Zhang, et al., “Classification of polarimetric SAR image based on support vector machine using multiplecomponent scattering model and texture features”, EURASIP J. Adv. Signal Process., Vol.2010, No.960831, pp.1-9, 2010.
    S.T. Tu, J.Y. Chen, W. Yang, et al., “Laplacian eigenmapsbased polarimetric dimensionality reduction for SAR image classification”, IEEE Trans. Geosci. Remote Sens., Vol.50, No.1, pp.170-179, 2012.
    S. Wang, K. Liu, J. Pei, et al., “Unsupervised classification of fully polarimetric SAR images based on scattering power entropy and copolarized ratio”, IEEE Geosci. Remote Sens. Lett., Vol.10, No.3, pp.622-626, 2013.
    Q. Chen, Y. Jiang, J. Lu, et al., “A new scattering-identification based unsupervised terrain classification for POLSAR image”, Chinese Journal of Electronics, Vol.39, No.3, pp.613-618, 2011.
    Q. Chen, Y. Jiang, J. Lu, et al., “A new algorithm for SAR imagery similarity measure based on local gradient ratio pattern”, Acta Electronica Sinica, Vol.38, No.12, pp.2729-2734, 2010. (In Chinese)
    S.W. Chen, M. Ohki, M. Shimada, et al., “Deorientation effect investigation for model-based decomposition over oriented built-up areas”, IEEE Geosci. Remote Sens. Lett., Vol.10, No.2, pp.273-277, 2013.
    D. Xiang, Y. Ban and Y. Su, “Model-based decomposition with cross scattering for polarimetric SAR urban areas”, IEEE Geoscience and Remote Sensing Letters, Vol.12, No.12, pp.2496-2500, 2015.
    Z. Qi, A.G.-O. Yeh, X. Li, et al., “A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data”, Remote Sens. Environ., Vol.118, No.15, pp.21-39, 2012.
    L. Zhang, B. Zou, H. Cai, et al., “Multiple-component scattering model for polarimetric SAR image decomposition”, IEEE Geosci. Remote Sens. Lett., Vol.5, No.4, pp.603-607, 2008.
    T. Moriyama, S. Uratsuka, T. Umehara, et al., “Polarimetric SAR image analysis using model fit for urban structures”, IEICE Trans. Commun., Vol.E88-B, No.3, pp.1234-1242, 2005.
    S.H. Hong and S. Wdowinski, “Double-bounce component in cross-polarimetric SAR from a new scattering target decomposition”, IEEE Transactions on Geoscience and Remote Sensing, Vol.52, No.6, pp.3039-3051, 2014.
    W. An, Y. Cui and J. Yang, “Three-component model-based decomposition for polarimetric SAR data”, IEEE Trans. Geosci. Remote Sens., Vol.48, No.6, pp.2732-2739, 2010.
    A. Sato and Y. Yamaguchi, “Four-component scattering power decomposition with extended volume scattering model”, IEEE Geosci. Remote Sens. Lett., Vol.9, No.2, pp.166-170, 2012.
    Y. Yamaguchi, T. Moriyama, M. Ishido, et al., “Fourcomponent scattering model for polarimetric SAR image decomposition”, IEEE Trans. Geosci. Remote Sens., Vol.43, No.8, pp.1699-1706, 2005
    D.d. Ridder, O. Kouropteva and O. Okun, “Supervised locally linear embedding”, Proc. of Artificial Neural Networks and Neural Information, Istanbul, Turkey, pp.333-341, 2003.
    Y. Yamaguchi, A. Sato, W.-M. Boerner, et al., “Fourcomponent scattering power decomposition with rotation of coherency matrix”, IEEE Trans. Geosci. Remote Sens., Vol.49, No.6, pp.2251-2258, 2011.
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