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Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification
Zhou Y.1; Wei Y.2
2016
Source PublicationIEEE Transactions on Cybernetics
ISSN21682267
Volume46Issue:7Pages:1667
Abstract

This paper proposes a spectral-spatial feature learning (SSFL) method to obtain robust features of hyperspectral images (HSIs). It combines the spectral feature learning and spatial feature learning in a hierarchical fashion. Stacking a set of SSFL units, a deep hierarchical model called the spectral-spatial networks (SSN) is further proposed for HSI classification. SSN can exploit both discriminative spectral and spatial information simultaneously. Specifically, SSN learns useful high-level features by alternating between spectral and spatial feature learning operations. Then, kernel-based extreme learning machine (KELM), a shallow neural network, is embedded in SSN to classify image pixels. Extensive experiments are performed on two benchmark HSI datasets to verify the effectiveness of SSN. Compared with state-of-the-art methods, SSN with a deep hierarchical architecture obtains higher classification accuracy in terms of the overall accuracy, average accuracy, and kappa (κ) coefficient of agreement, especially when the number of the training samples is small. © 2015 IEEE.

KeywordHierarchical Learning Hyperspectral Image Classification Kernel-based Extreme Learning Machine Spectral-spatial Feature
DOI10.1109/TCYB.2015.2453359
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000379757900015
The Source to ArticleScopus
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Cited Times [WOS]:37   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Information Science, University of Macau, Macau 999078, China
2.School of Educational Information Technology, Central China Normal University, Wuhan 430079, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhou Y.,Wei Y.. Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification[J]. IEEE Transactions on Cybernetics,2016,46(7):1667.
APA Zhou Y.,&Wei Y..(2016).Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification.IEEE Transactions on Cybernetics,46(7),1667.
MLA Zhou Y.,et al."Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification".IEEE Transactions on Cybernetics 46.7(2016):1667.
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