Noise-robust subband decomposition blind signal separation for hyperspectral unmixing

Abstract

Hyperspectral unmixing can be considered as a blind source separation (BSS) and/or independent component analysis (ICA) problem. This paper presents a new noise-resistant subband decomposition BSS/ICA approach for hyperspectral unmixing. Subband decomposition BSS relaxes the assumption that the source signals are mutual independent, which has been proved successful in some BSS applications. However, the existing subband decomposition and subband selection methods emphasize the “independence” of sub-components, but ignore the impact of their “noise”. It is well known that most subband decomposition such as wavelet and fourier transforms have been successfully used for noise removal, so simultaneously considering independence and noise through subband decomposition is possible. In this paper, we propose wavelet package transform for subband decomposition, independence-and-noise joint measure based ranking method for subband selection. The experimental results indicate that the proposed methods are promising in hyperspectral unmixing.