Hyperspectral Images Denoising Based on Mixtures of Factor Analyzers

This paper presents two hyperspectral image (HSI) denoising methods, mixtures of factor analyzers (MFA) and wavelet-based MFA (WMFA). MFA uses a Gaussian mixture model to segment the original HSI into different parts, where each part follows Gaussian distribution and then utilizes a factor analyzer to get a low-rank factor loading matrix, and finally uses the inverse transformation of the matrix to get the denoised hyperspectral dataset. WMFA uses the MFA in the wavelet domain to remove the noise in HSI. The proposed methods are evaluated by using both simulated and real hyperspectral datasets.