A Robust Sparse Representation Model for Hyperspectral Image Classification

Sensors

Shaoguang Huang¹  Aleksandra Pižurica¹  Hongyan Zhang² 

[1] Department of Telecommunications and Information Processing, Ghent University, Sint Pietersnieuwstraat 41, 9000 Gent, Belgium;The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan 430079, China;

关键词: robust classification;    hyperspectral image;    super-pixel segmentation;    sparse representation;   

DOI  :  10.3390/s17092087

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown