Spectral-spatial classification of hyperspectral images using wavelet transform and hidden Markov random fields |
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Authors: | Elham Kordi Ghasrodashti Habibollah Danyali |
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Institution: | Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran |
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Abstract: | This paper proposes a spectral–spatial method for classification of hyperspectral images. The proposed method, called SSC, consists of two steps. In the first step, to overcome the computation complexity, a wavelet-based classifier is designed. In the second step, to enhance the classification accuracy, a novel hidden Markov random field called NHMRF technique in spatial domain is suggested. In NHMRF, we convert two-dimensional energies of traditional hidden Markov random field to three-dimensional energies and then we apply edge preserving regularization terms on each two-dimensional energy of this cube. The class label of each test pixel is fixed based on minimum three-dimensional energy achieved by edge preserving regularization terms. Experimental results show that the classification accuracy of the proposed approach based on three-dimensional energies and edge preserving regularization terms is effectively improved in comparison with the state-of-the-art methods. |
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Keywords: | Hyperspectral image hidden Markov random fields edge preserving regularization term wavelet transform |
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