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Robust joint sparsity model for hyperspectral image classification

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Abstract
Sparsity-based classification methods have been widely used in hyperspectral image (HSI) classification. These methods typically assumed Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust super-pixel level joint sparse representation classification model (RSJSRC) to address the mixed noise problem in sparsity-based HSI classification. Our method takes into account both Gaussian and sparse noise. Experimental results on simulated and real data demonstrate the efficiency of the proposed method and clear benefits from the introduced mixed-noise model.
Keywords
Robust classification, hyperspectral image, super-pixel segmentation, sparse representation

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Please use this url to cite or link to this publication:

MLA
Huang, Shaoguang et al. “Robust Joint Sparsity Model for Hyperspectral Image Classification.” IEEE, 2017. 3130–3134. Print.
APA
Huang, Shaoguang, Zhang, H., Liao, W., & Pizurica, A. (2017). Robust joint sparsity model for hyperspectral image classification (pp. 3130–3134). Presented at the 24th IEEE International Conference on Image Processing (ICIP) , IEEE.
Chicago author-date
Huang, Shaoguang, Hongyan Zhang, Wenzhi Liao, and Aleksandra Pizurica. 2017. “Robust Joint Sparsity Model for Hyperspectral Image Classification.” In , 3130–3134. IEEE.
Chicago author-date (all authors)
Huang, Shaoguang, Hongyan Zhang, Wenzhi Liao, and Aleksandra Pizurica. 2017. “Robust Joint Sparsity Model for Hyperspectral Image Classification.” In , 3130–3134. IEEE.
Vancouver
1.
Huang S, Zhang H, Liao W, Pizurica A. Robust joint sparsity model for hyperspectral image classification. IEEE; 2017. p. 3130–4.
IEEE
[1]
S. Huang, H. Zhang, W. Liao, and A. Pizurica, “Robust joint sparsity model for hyperspectral image classification,” presented at the 24th IEEE International Conference on Image Processing (ICIP) , Beijing, 2017, pp. 3130–3134.
@inproceedings{8529659,
  abstract     = {Sparsity-based classification methods have been widely used in hyperspectral image (HSI) classification. These methods typically assumed Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust super-pixel level joint sparse representation classification model (RSJSRC) to address the mixed noise problem in sparsity-based HSI classification. Our method takes into account both Gaussian and sparse noise. Experimental results on simulated and real data demonstrate the efficiency of the proposed method and clear benefits from the introduced mixed-noise model.},
  author       = {Huang, Shaoguang and Zhang, Hongyan and Liao, Wenzhi and Pizurica, Aleksandra},
  isbn         = {978-1-5090-2175-8},
  issn         = {1522-4880 },
  keywords     = {Robust classification,hyperspectral image,super-pixel segmentation,sparse representation},
  language     = {eng},
  location     = {Beijing},
  pages        = {3130--3134},
  publisher    = {IEEE},
  title        = {Robust joint sparsity model for hyperspectral image classification},
  year         = {2017},
}

Web of Science
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