Abstract
This paper proposes a novel hyperspectral image (HSI) classification method based on sparse model, which incorporates the spectral and spatial information of the sparse coefficient. Firstly, a sparse dictionary is built by using the training samples and the sparse coefficient is obtained through the sparse representation method. Secondly, a probability map for each class is established by summing the sparse coefficients of each class. Thirdly, the mean filtering is applied on each probability map to exploit the spatial information. Finally, we compare the probability map to find the maximum probability for each pixel and then determine the class label of each pixel. Experimental results demonstrate the effectiveness of the proposed method.
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Liu, D., Li, S., Fang, L. (2014). Hyperspectral Image Classification by Exploiting the Spectral-Spatial Correlations in the Sparse Coefficients. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_16
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DOI: https://doi.org/10.1007/978-3-662-45646-0_16
Publisher Name: Springer, Berlin, Heidelberg
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