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)

Volume: 9772

Pages: 192-201

eISSN: 1611-3349

ISBN: 9783319422930

ISSN: 0302-9743

DOI: 10.1007/978-3-319-42294-7_16


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.

Source: Scopus