Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 49, Issue 7, Pages 2406-2419Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2810806
Keywords
Discriminant analysis; feature learning; hypergraph learning; hyperspectral image (HSI) classification; spatial-spectral information
Categories
Funding
- National Science Foundation of China [61471274, 41431175, 61711530239, 61771349]
- Australian Research Council [DP-180103424, FL-170100117, DP-140102164, FT-130101457]
- National Post-Doctoral Program for Innovative Talents [BX201700182]
- China Post-Doctoral Science Foundation [2017M622521]
- Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education [JYB201703]
- LIESMARS Special Research Funding
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Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are mostly based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.
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