Robust joint sparsity model for hyperspectral image classification


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.

2017 24th IEEE International Conference on Image Processing (ICIP)
Shaoguang Huang
Postdoctoral researcher

My research interests include image processing, sparse modelling and hyperspectral image analysis.