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Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection
Open AccessArticle

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

1
School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
2
School of Artificial Intelligence, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1485; https://doi.org/10.3390/rs11121485
Received: 16 May 2019 / Revised: 16 June 2019 / Accepted: 17 June 2019 / Published: 22 June 2019
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper. By regarding the raw cube hyperspectral image as the only tensor sample, T-MLRD needs no labeled samples and avoids the processing of constructing tensor samples. In addition, a novel multiscale low rank estimating method is proposed to obtain the optimal rank along each mode of hyperspectral image which avoids the complicated rank computing. Finally, the multiscale low rank feature representation is fused to achieve dimensionality reduction. Experimental results on real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches. View Full-Text
Keywords: dimensionality reduction; hyperspectral images classification; multiscale; low rank dimensionality reduction; hyperspectral images classification; multiscale; low rank
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An, J.; Lei, J.; Song, Y.; Zhang, X.; Guo, J. Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction. Remote Sens. 2019, 11, 1485.

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