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Denoising of hyperspectral imagery using an intrinsic spectral representation model with spatial smoothness constraint

Efficient denoising of hyperspectral imagery (HSI) relies on an representational model that is capable of capturing the spatial and spectral correlation in HSI. Recently, an intrinsic representation (IR) approach based on the linear spectral mixture model (LSMM) was proposed for unsupervised feature extraction. The IR model constitutes a sound representational model due to its ability to account for the physical data generation process of HSI, the spatial correlation effect, and the noise variance heterogeneity effect. In this paper, we explore the potential of IR for the denoising of HSI. A noisy pixel in HSI is expressed as a nonnegative linear combination of several endmembers, plus some Gaussian noise with heterogeneous noise variances. In order to perform denoising, the IR approach is used to adaptively estimate both the endmembers and the nonnegative coefficients (i.e., the abundances), which are finally used to reconstruct the clean image. The experiments on both simulated and real hyperspectral images demonstrate that the IR approach not only can resist the influence of noise, but also can preserve the image details.