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Open AccessArticle

Discriminant Analysis with Graph Learning for Hyperspectral Image Classification

by 1, 1,2,* and 3,4
1
School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an 710072, China
2
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
3
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
4
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 836; https://doi.org/10.3390/rs10060836
Received: 26 April 2018 / Revised: 24 May 2018 / Accepted: 24 May 2018 / Published: 27 May 2018
Linear Discriminant Analysis (LDA) is a widely-used technique for dimensionality reduction, and has been applied in many practical applications, such as hyperspectral image classification. Traditional LDA assumes that the data obeys the Gaussian distribution. However, in real-world situations, the high-dimensional data may be with various kinds of distributions, which restricts the performance of LDA. To reduce this problem, we propose the Discriminant Analysis with Graph Learning (DAGL) method in this paper. Without any assumption on the data distribution, the proposed method learns the local data relationship adaptively during the optimization. The main contributions of this research are threefold: (1) the local data manifold is captured by learning the data graph adaptively in the subspace; (2) the spatial information within the hyperspectral image is utilized with a regularization term; and (3) an efficient algorithm is designed to optimize the proposed problem with proved convergence. Experimental results on hyperspectral image datasets show that promising performance of the proposed method, and validates its superiority over the state-of-the-art. View Full-Text
Keywords: hyperspectral image classification; linear discriminant analysis; graph learning; sparse learning hyperspectral image classification; linear discriminant analysis; graph learning; sparse learning
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Chen, M.; Wang, Q.; Li, X. Discriminant Analysis with Graph Learning for Hyperspectral Image Classification. Remote Sens. 2018, 10, 836.

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