Robust joint sparsity model for hyperspectral image classification

Zhang, Hongyan; Liao, Wenzhi; Pižurica, Aleksandra

doi:10.1109/icip.2017.8296859

Cited by 2 publications

(1 citation statement)

References 28 publications

(33 reference statements)

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“…The results on simulated and real data demonstrate improved performance in comparison to recent related methods and a clear benefit resulting from the introduced robust model. Parts of this work have been accepted for presentation at a conference [ 40 ]. In comparison to the conference version, here we give more elaborate presentation and analysis of the method.…”

Section: Introductionmentioning

confidence: 99%

A Robust Sparse Representation Model for Hyperspectral Image Classification

Zhang

Pižurica

2017

Sensors

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Sparse representation has been extensively investigated for hyperspectral image (HSI) classification and led to substantial improvements in the performance over the traditional methods, such as support vector machine (SVM). However, the existing sparsity-based classification methods typically assume Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust classification model that admits realistic mixed noise, which includes Gaussian noise and sparse noise. We combine a model for mixed noise with a prior on the representation coefficients of input data within a unified framework, which produces three kinds of robust classification methods based on sparse representation classification (SRC), joint SRC and joint SRC on a super-pixels level. Experimental results on simulated and real data demonstrate the effectiveness of the proposed method and clear benefits from the introduced mixed-noise model.

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Section: Introductionmentioning

confidence: 99%