Title | Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification |
Author | |
Publication Years | 2022
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DOI | |
Source Title | |
ISSN | 1939-1404
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EISSN | 2151-1535
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Volume | 15Pages:7741-7754 |
Abstract | Given the detrimental effect of spectral variations in a hyperspectral image (HSI), this paper investigates to recover its discriminative representation to improve the classification performance. We propose a new method, namely local low-rank approximation with superpixel-guided locality preserving graph (LLRA-SLPG), which can reduce the spectral variations and preserve the local manifold structure of an HSI. Specifically, the LLRA-SLPG method first clusters pixels of an HSI into several groups (i.e., superpixels). By taking advantage of the local manifold structure, a Laplacian graph is constructed from the superpixels to ensure that a typical pixel should be similar to its neighbors within the same superpixel. The LLRA-SLPG model can increase the compactness of pixels belonging to the same class by reducing spectral variations while promoting local consistency via the Laplacian graph. The objective function of the LLRA-SLPG model can be solved efficiently in an iterative manner. Experimental results on four benchmark datasets validate the superiority of the LLRA-SLPG model over state-of-the-art methods, particularly in cases where only extremely few training pixels are available. |
Keywords | |
URL | [Source Record] |
Indexed By | |
Language | English
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SUSTech Authorship | First
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EI Accession Number | 20223512671990
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EI Keywords | Computer programming
; Hyperspectral imaging
; Image classification
; Image enhancement
; Image segmentation
; Iterative methods
; Laplace transforms
; Linear programming
; Spectroscopy
; Structure (composition)
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ESI Classification Code | Computer Programming:723.1
; Data Processing and Image Processing:723.2
; Imaging Techniques:746
; Mathematical Transformations:921.3
; Numerical Methods:921.6
; Materials Science:951
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Scopus EID | 2-s2.0-85136886123
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Data Source | Scopus
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PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9861684 |
Citation statistics |
Cited Times [WOS]:1
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/401672 |
Department | Department of Computer Science and Engineering |
Affiliation | 1.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China 2.Department of Computer Science and Engineering, Southern University of Science and Technology, China 3.School of Computer Science and Engineering, Southeast University, Nanjing, China |
First Author Affilication | Department of Computer Science and Engineering |
First Author's First Affilication | Department of Computer Science and Engineering |
Recommended Citation GB/T 7714 |
Yang,Shujun,Zhang,Yu,Jia,Yuheng,et al. Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2022,15:7741-7754.
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APA |
Yang,Shujun,Zhang,Yu,Jia,Yuheng,&Zhang,Weijia.(2022).Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,15,7741-7754.
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MLA |
Yang,Shujun,et al."Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15(2022):7741-7754.
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