15 November 2011 Unsupervised hyperspectral imagery classification via sparse multi-way models and image fusion
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Proceedings Volume 8335, 2012 International Workshop on Image Processing and Optical Engineering; 83351U (2011) https://doi.org/10.1117/12.917563
Event: 2012 International Workshop on Image Processing and Optical Engineering, 2012, Harbin, China
Abstract
Inspired by the recent rapid progress of l1-norm minimization techniques and the great success of sparse dictionary learning in image modeling, this paper proposes a sparse multi-way models clustering fusion technique to improve the classification performance in hyperspectral imagery. Multi-way models consider hyperspectral imagery data as a whole entity to treat jointly spatial and spectral modes. The whole clustering fusion method is composed three steps. Firstly, the complete hyperspectral data is grouped into several independent sub-band data sources. Then, sparse multi-way model is used to feature extraction in every band set, and divide the scene into a series of homomorphic regions. At last, we propose a fusion method to combine the information provided by each band set, it can acquire approximate supervised classification performance (such as K-nearest Neighbor classifier).The experimental results on the HYDICE imagery demonstrate the efficiency and superiority of the proposed clustering method to the classical K-means clustering method.
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Yongqiang Zhao, Jinxiang Yang, Qingyong Zhang, Lin Song, "Unsupervised hyperspectral imagery classification via sparse multi-way models and image fusion", Proc. SPIE 8335, 2012 International Workshop on Image Processing and Optical Engineering, 83351U (15 November 2011); doi: 10.1117/12.917563; https://doi.org/10.1117/12.917563
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