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Proceedings Paper

Unsupervised hyperspectral imagery classification via sparse multi-way models and image fusion
Author(s): Yongqiang Zhao; Jinxiang Yang; Qingyong Zhang; Lin Song
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Paper 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.

Paper Details

Date Published: 15 November 2011
PDF: 7 pages
Proc. SPIE 8335, 2012 International Workshop on Image Processing and Optical Engineering, 83351U (15 November 2011); doi: 10.1117/12.917563
Show Author Affiliations
Yongqiang Zhao, Northwestern Polytechnical Univ. (China)
Jinxiang Yang, Northwestern Polytechnical Univ. (China)
Qingyong Zhang, Northwestern Polytechnical Univ. (China)
Lin Song, Northwestern Polytechnical Univ. (China)

Published in SPIE Proceedings Vol. 8335:
2012 International Workshop on Image Processing and Optical Engineering
Hai Guo; Qun Ding, Editor(s)

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