SCSIO OpenIR  > 热带海洋环境国家重点实验室(LTO)
Learning Discriminative Sparse Representations for Hyperspectral Image Classification
Du, Peijun; Xue, Zhaohui; Li, Jun; Plaza, Antonio; xzh2012@163.com
2015
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
Volume9Issue:6Pages:1089-1104
Subtype1932-4553
AbstractIn sparse representation (SR) driven hyperspectral image classification, signal-to-reconstruction rule-based classification may lack generalization performance. In order to overcome this limitation, we presents a new method for discriminative sparse representation of hyperspectral data by learning a reconstructive dictionary and a discriminative classifier in a SR model regularized with total variation (TV). The proposed method features the following components. First, we adopt a spectral unmixing by variable splitting augmented Lagrangian and TV method to guarantee the spatial homogeneity of sparse representations. Second, we embed dictionary learning in the method to enhance the representative power of sparse representations via gradient descent in a class-wise manner. Finally, we adopt a sparse multinomial logistic regression (SMLR) model and design a class-oriented optimization strategy to obtain a powerful classifier, which improves the performance of the learnt model for specific classes. The first two components are beneficial to produce discriminative sparse representations. Whereas, adopting SMLR allows for effectively modeling the discriminative information. Experimental results with both simulated and real hyperspectral data sets in a number of experimental comparisons with other related approaches demonstrate the superiority of the proposed method.
Department[Du, Peijun; Xue, Zhaohui] Nanjing Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Key Lab Satellite Mapping Technol & Applicat,Natl, Nanjing 210023, Jiangsu, Peoples R China; [Du, Peijun; Xue, Zhaohui] Nanjing Univ, Collaborat Innovat Ctr South China Sea Studies, Nanjing 210023, Jiangsu, Peoples R China; [Li, Jun] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China; [Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10071, Spain ; LTO
KeywordHyperspectral Image Classification Discriminative Sparse Representation (Dsr) Total Variation (Tv) Dictionary Learning Sparse Multinomial Logistic Regression (Smlr)
Subject AreaEngineering
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.scsio.ac.cn/handle/344004/15039
Collection热带海洋环境国家重点实验室(LTO)
Corresponding Authorxzh2012@163.com
Recommended Citation
GB/T 7714
Du, Peijun,Xue, Zhaohui,Li, Jun,et al. Learning Discriminative Sparse Representations for Hyperspectral Image Classification[J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,2015,9(6):1089-1104.
APA Du, Peijun,Xue, Zhaohui,Li, Jun,Plaza, Antonio,&xzh2012@163.com.(2015).Learning Discriminative Sparse Representations for Hyperspectral Image Classification.IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,9(6),1089-1104.
MLA Du, Peijun,et al."Learning Discriminative Sparse Representations for Hyperspectral Image Classification".IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 9.6(2015):1089-1104.
Files in This Item:
File Name/Size DocType Version Access License
Learning Discriminat(5126KB)期刊论文作者接受稿开放获取CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.