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Article

Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

1
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
The Key Laboratory on Opto-electronic Technique and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lefei Zhang, Liangpei Zhang, Qian Shi and Yanni Dong
Received: 10 February 2021 / Revised: 26 March 2021 / Accepted: 29 March 2021 / Published: 2 April 2021
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification. View Full-Text
Keywords: hyperspectral image; graph learning; dimensionality reduction; collaborative representation; local neighborhood structure hyperspectral image; graph learning; dimensionality reduction; collaborative representation; local neighborhood structure
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MDPI and ACS Style

Shi, G.; Luo, F.; Tang, Y.; Li, Y. Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding. Remote Sens. 2021, 13, 1363. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071363

AMA Style

Shi G, Luo F, Tang Y, Li Y. Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding. Remote Sensing. 2021; 13(7):1363. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071363

Chicago/Turabian Style

Shi, Guangyao, Fulin Luo, Yiming Tang, and Yuan Li. 2021. "Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding" Remote Sensing 13, no. 7: 1363. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071363

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