Abstract
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.
Highlights
Unlike classical multispectral images, hyperspectral images (HSIs) provide richer spectral information about the image objects in hundreds of narrow bands
A HSI is captured as a three-dimensional data cube comprising two-dimensional spatial information and one-dimensional spectral information
We develop here a robust joint sparse representation classification (JSRC) model, where the spatial information is captured at a super-pixel level, instead of using fixed-size rectangular neighbourhoods
Summary
Hyperspectral images (HSIs) provide richer spectral information about the image objects in hundreds of narrow bands. A HSI is captured as a three-dimensional data cube comprising two-dimensional spatial information and one-dimensional spectral information. The spectral signature of a pixel is a vector whose entries correspond to spectral responses in different bands. Different materials have diverse spectral signatures, hyperspectral imaging allows differentiation between materials that are often visually indistinguishable. Numerous application areas include agriculture [1,2], defense and security [3] and environmental monitoring [4,5]. Classification of HSIs currently enjoys huge interest in the remote sensing community
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