Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification
We propose a kernel nonlocal joint collaborative representation classification method based on column generation for hyperspectral imagery. The proposed approach first maps the original spectral space to a higher implicit kernel space by directly taking the similarity measures between spectral pixel...
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| Published in | ISPRS journal of photogrammetry and remote sensing Vol. 94; pp. 25 - 36 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.08.2014
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0924-2716 1872-8235 |
| DOI | 10.1016/j.isprsjprs.2014.04.014 |
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| Abstract | We propose a kernel nonlocal joint collaborative representation classification method based on column generation for hyperspectral imagery. The proposed approach first maps the original spectral space to a higher implicit kernel space by directly taking the similarity measures between spectral pixels as a feature, and then utilizes a nonlocal joint collaborative regression model for kernel signal reconstruction and the subsequent pixel classification. We also develop two kinds of specific radial basis function kernels for measuring the similarities. The experimental results indicate that the proposed algorithms obtain a competitive performance and outperform other state-of-the-art regression-based classifiers and the classical support vector machines classifier. |
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| AbstractList | We propose a kernel nonlocal joint collaborative representation classification method based on column generation for hyperspectral imagery. The proposed approach first maps the original spectral space to a higher implicit kernel space by directly taking the similarity measures between spectral pixels as a feature, and then utilizes a nonlocal joint collaborative regression model for kernel signal reconstruction and the subsequent pixel classification. We also develop two kinds of specific radial basis function kernels for measuring the similarities. The experimental results indicate that the proposed algorithms obtain a competitive performance and outperform other state-of-the-art regression-based classifiers and the classical support vector machines classifier. |
| Author | Zhang, Hongyan Li, Jiayi Zhang, Liangpei |
| Author_xml | – sequence: 1 givenname: Jiayi surname: Li fullname: Li, Jiayi – sequence: 2 givenname: Hongyan surname: Zhang fullname: Zhang, Hongyan – sequence: 3 givenname: Liangpei surname: Zhang fullname: Zhang, Liangpei email: zlp62@whu.edu.cn |
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| Copyright | 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) 2015 INIST-CNRS |
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| Keywords | Joint collaboration model Column generation Kernel method Hyperspectral image classification experimental studies algorithms models maps joints classification Image signals regression imagery performances Pixel |
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| SubjectTerms | Algorithms Animal, plant and microbial ecology Applied geophysics Biological and medical sciences Classification Classifiers Column generation Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Hyperspectral image classification hyperspectral imagery Internal geophysics Joint collaboration model Kernel method Kernels Pixels regression analysis Representations Similarity Spectra support vector machines Teledetection and vegetation maps |
| Title | Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification |
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