Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification
Joint Authors
Qin, Zhen-tao
Yang, Wu-nian
Yang, Ru
Zhao, Xiang-yu
Yang, Teng-jiao
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-22
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
This paper presents a new, dictionary-based method for hyperspectral image classification, which incorporates both spectral and contextual characteristics of a sample clustered to obtain a dictionary of each pixel.
The resulting pixels display a common sparsity pattern in identical clustered groups.
We calculated the image’s sparse coefficients using the dictionary approach, which generated the sparse representation features of the remote sensing images.
The sparse coefficients are then used to classify the hyperspectral images via a linear SVM.
Experiments show that our proposed method of dictionary-based, clustered sparse coefficients can create better representations of hyperspectral images, with a greater overall accuracy and a Kappa coefficient.
American Psychological Association (APA)
Qin, Zhen-tao& Yang, Wu-nian& Yang, Ru& Zhao, Xiang-yu& Yang, Teng-jiao. 2015. Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification. Journal of Spectroscopy،Vol. 2015, no. 2015, pp.1-6.
https://search.emarefa.net/detail/BIM-1070326
Modern Language Association (MLA)
Qin, Zhen-tao…[et al.]. Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification. Journal of Spectroscopy No. 2015 (2015), pp.1-6.
https://search.emarefa.net/detail/BIM-1070326
American Medical Association (AMA)
Qin, Zhen-tao& Yang, Wu-nian& Yang, Ru& Zhao, Xiang-yu& Yang, Teng-jiao. Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification. Journal of Spectroscopy. 2015. Vol. 2015, no. 2015, pp.1-6.
https://search.emarefa.net/detail/BIM-1070326
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1070326