Robust joint sparsity model for hyperspectral image classification

Shaoguang Huang, Hongyan Zhang, Wenzhi Liao, Aleksandra Pizurica

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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.
Original languageEnglish
Pages3130-3134
Number of pages5
DOIs
Publication statusPublished - 22 Feb 2018
Event24th IEEE International Conference on Image Processing (ICIP) 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Conference

Conference24th IEEE International Conference on Image Processing (ICIP) 2017
Abbreviated titleICIP2017
CountryChina
CityBeijing
Period17/09/1720/09/17

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Keywords

  • robust classification
  • hyperspectral image
  • super-pixel segmentation
  • sparse representation
  • gaussian noise
  • sparse matrices
  • optimization
  • image classification
  • geophysical image processing

Cite this

Huang, S., Zhang, H., Liao, W., & Pizurica, A. (2018). Robust joint sparsity model for hyperspectral image classification. 3130-3134. Paper presented at 24th IEEE International Conference on Image Processing (ICIP) 2017, Beijing, China. https://doi.org/10.1109/ICIP.2017.8296859