Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Independent Component Analysis: A Tutorial Introduction
Independent Component Analysis: A Tutorial Introduction
A Note on Stone's Conjecture of Blind Signal Separation
Neural Computation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Hyperspectral imagery (HSI) unmixing is a process that decomposes pixel spectra into a collection of constituent spectra (endmembers) and their correspondent abundance fractions. Without knowing any knowledge of HSI data, the unmixing problem is transformed into a blind source separation (BSS) problem. Several methods have been proposed to deal with the problem, like independent component analysis (ICA). In this paper, we introduce spatial complexity that applies Markov random field (MRF) to characterize the spatial correlation information of abundance fractions. Compared to previous BSS techniques for HSI unmixing, the major advantage of our approach is that it totally considers HSI spatial structure. Additionally, a proof is given that spatial complexity is suitable for HSI unmixing. Encouraging results have been obtained in terms of unmixing accuracy, suggesting the effectiveness of our approach.