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Open AccessArticle

Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction

1
School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
2
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1822; https://doi.org/10.3390/rs11151822
Received: 1 July 2019 / Revised: 26 July 2019 / Accepted: 29 July 2019 / Published: 4 August 2019
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
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Abstract

Dimensionality reduction is of great importance which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images. Tensor analysis which can retain both spatial and spectral information of hyperspectral images has caused more and more concern in the field of hyperspectral images processing. In general, a desirable low dimensionality feature representation should be discriminative and compact. To achieve this, a tensor discriminant analysis model via compact feature representation (TDA-CFR) was proposed in this paper. In TDA-CFR, the traditional linear discriminant analysis was extended to tensor space to make the resulting feature representation more informative and discriminative. Furthermore, TDA-CFR redefines the feature representation of each spectral band by employing the tensor low rank decomposition framework which leads to a more compact representation. View Full-Text
Keywords: dimensionality reduction; hyperspectral images classification; tensor; discriminant analysis dimensionality reduction; hyperspectral images classification; tensor; discriminant analysis
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An, J.; Song, Y.; Guo, Y.; Ma, X.; Zhang, X. Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction. Remote Sens. 2019, 11, 1822.

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