Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction
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 yield...
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doaj-c2f2b00f34194affb0957fbc461003172020-11-25T01:09:00ZengMDPI AGRemote Sensing2072-42922019-06-011112148510.3390/rs11121485rs11121485Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality ReductionJinliang An0Jinhui Lei1Yuzhen Song2Xiangrong Zhang3Jinmei Guo4School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Artificial Intelligence, Xidian University, Xi’an 710071, ChinaSchool of Artificial Intelligence, Xidian University, Xi’an 710071, ChinaDimensionality 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.https://www.mdpi.com/2072-4292/11/12/1485dimensionality reductionhyperspectral images classificationmultiscalelow rank |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jinliang An Jinhui Lei Yuzhen Song Xiangrong Zhang Jinmei Guo |
spellingShingle |
Jinliang An Jinhui Lei Yuzhen Song Xiangrong Zhang Jinmei Guo Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction Remote Sensing dimensionality reduction hyperspectral images classification multiscale low rank |
author_facet |
Jinliang An Jinhui Lei Yuzhen Song Xiangrong Zhang Jinmei Guo |
author_sort |
Jinliang An |
title |
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction |
title_short |
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction |
title_full |
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction |
title_fullStr |
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction |
title_full_unstemmed |
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction |
title_sort |
tensor based multiscale low rank decomposition for hyperspectral images dimensionality reduction |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-06-01 |
description |
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. |
topic |
dimensionality reduction hyperspectral images classification multiscale low rank |
url |
https://www.mdpi.com/2072-4292/11/12/1485 |
work_keys_str_mv |
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1725180477166321664 |