期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 17, 期 6, 页码 1082-1086出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2936652
关键词
Hyperspectral imaging; Sparse matrices; Dimensionality reduction; STEM; Euclidean distance; Dimensionality reduction; hypergraph learning; hyperspectral image (HSI); sparse representation
类别
资金
- National Science Foundation of China [41431175, 61801336]
- Science and Technology Research Program of the Chongqing Municipal Education Commission [KJQN201800632]
- Open Research Fund of Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University [2019LSDMIS06]
- Open Research Fund of Hubei Key Laboratory of Applied Mathematics (Hubei University) [HBAM201803]
- Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences [2017LDE002]
- National Postdoctoral Program for Innovative Talents [BX201700182]
- China Postdoctoral Science Foundation [2017M622521]
Hyperspectral image (HSI) contains complex multiple structures. Therefore, the key problem analyzing the intrinsic properties of an HSI is how to represent the structure relationships of the HSI effectively. Hypergraph is very effective to describe the intrinsic relationships of the HSI. In general, Euclidean distance is adopted to construct the hypergraph. However, this method cannot effectively represent the structure properties of high-dimensional data. To address this problem, we propose a sparse-adaptive hypergraph discriminant analysis (SAHDA) method to obtain the embedding features of the HSI in this letter. SAHDA uses the sparse representation to reveal the structure relationships of the HSI adaptively. Then, an adaptive hypergraph is constructed by using the intraclass sparse coefficients. Finally, we develop an adaptive dimensionality reduction mode to calculate the weights of the hyperedges and the projection matrix. SAHDA can adaptively reveal the intrinsic properties of the HSI and enhance the performance of the embedding features. Some experiments on the Washington DC Mall hyperspectral data set demonstrate the effectiveness of the proposed SAHDA method, and SAHDA achieves better classification accuracies than the traditional graph learning methods.
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