Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity
1
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
2
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
3
Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lizhe Wang, Liping Di, Peng Liu, Richard Müller and Prasad S. Thenkabail
Remote Sens. 2017, 9(4), 323; https://doi.org/10.3390/rs9040323
Received: 24 January 2017 / Revised: 17 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS) measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA).
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Feng, F.; Li, W.; Du, Q.; Zhang, B. Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity. Remote Sens. 2017, 9, 323. https://doi.org/10.3390/rs9040323
AMA Style
Feng F, Li W, Du Q, Zhang B. Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity. Remote Sensing. 2017; 9(4):323. https://doi.org/10.3390/rs9040323
Chicago/Turabian StyleFeng, Fubiao; Li, Wei; Du, Qian; Zhang, Bing. 2017. "Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity" Remote Sens. 9, no. 4: 323. https://doi.org/10.3390/rs9040323
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