Local Spatial-Spectral Correlation Based Mixtures of Factor Analyzers for Hyperspectral Denoising

This paper presents a local spatial-spectral correlation based mixtures of factor analyzers (LSSC-MFA) denoising method for hyperspectral image (HSI). HSIs are usually degraded by different noise types such as missing lines (ML), missing pixels (MP), salt and pepper noise (SP), and Gaussian noise. The proposed method, hierarchically, removes the mixed noise. Firstly, we develop a novel local spatial-spectral correlation (LSSC) method to remove the ML noise. Then LSSC-MFA uses the mixtures of factor analyzers (MFA) method to remove the MP, SP, and Gaussian noises. The performance of the proposed method has been validated using both real and simulated HSI datasets. Results on the simulated datasets confirm considerable improvements in terms of peak signal-to-noise ratio (PSNR) compared to the state-of-the-art denoising methods used in experiments. In addition, visual improvements can be observed in the case of real dataset experiments.