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Remote Sens. 2017, 9(4), 323;

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
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
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)
View Full-Text   |   Download PDF [1588 KB, uploaded 30 March 2017]   |  


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). View Full-Text
Keywords: hyperspectral data; dimensionality reduction; graph embedding; spectral similarity hyperspectral data; dimensionality reduction; graph embedding; spectral similarity

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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. (CC BY 4.0).

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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.

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