Blind hyperspectral unmixing using deep-independent information
Authors: Wang, F., Li, R., Zhang, J. and Jiang, L.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
In linear mixing model (LMM), the endmember fractional abundances should satisfy the sum-to-one constraint, which makes the well-known independent component analysis (ICA) based blind source separation (BSS) algorithms not well suited to blind hyperspectral unmixing (bHU) problem. A novel framework for bHU consulting dependent component analysis (DCA) is presented in this paper. By using the idea of subband decomposition, wavelet packet decomposition based bHU algorithm (termed as SDWP-bHU) is proposed, where the deep independent information of the source signals is exploited to fulfill the endmember signatures extraction and abundances separation tasks. Experiments based on the synthetic data are performed to evaluate the validity of the proposed approach.