Abstract
In this paper, a novel framework for an accurate spectral–spatial classification of hyperspectral images is proposed to address nonlinear classification problems. The algorithm is based on the spectral angle mapper (SAM), which is achieved by introducing the multi-center model and Markov random fields (MRF) into a probabilistic decision framework to obtain an accurate classification. Experimental comparisons between several traditional classification methods and the proposed MSAM–MRF algorithm have demonstrated that the performance of the proposed MSAM–MRF algorithm outperforms the traditional classification algorithms.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (No. 61475085), the Science and Technology Development Plans of Shandong Province (Nos. 2012GGE27073 and 2014GSF118142), the Science and Technology Development Plans of Jinan City and the Fundamental Research Funds of Shandong University (No. 2015JC038).
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Bo Tang and Xiaoyan Xiao contributed equally to this paper.
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Tang, B., Liu, Z., Xiao, X. et al. Spectral–spatial hyperspectral classification based on multi-center SAM and MRF. Opt Rev 22, 911–918 (2015). https://doi.org/10.1007/s10043-015-0139-9
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DOI: https://doi.org/10.1007/s10043-015-0139-9