中文版 | English
Title

Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification

Author
Publication Years
2022
DOI
Source Title
ISSN
1939-1404
EISSN
2151-1535
Volume15Pages: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
SUSTech Authorship
First
EI Accession Number
20223512671990
EI Keywords
Computer programming ; Hyperspectral imaging ; Image classification ; Image enhancement ; Image segmentation ; Iterative methods ; Laplace transforms ; Linear programming ; Spectroscopy ; Structure (composition)
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
Scopus EID
2-s2.0-85136886123
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9861684
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/401672
DepartmentDepartment 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 AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment 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.
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.
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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