Independent component analysis for spectral unmixing in hyperspectral remote sensing image - Luo, Wen-Fei; Zhong, Liang; Zhang, Bing; Gao, Lian-Ru (Guang pu xue yu guang pu fen xi, 2010)
Hyperspectral remote sensing plays an important role in earth observation on land, ocean and atmosphere. A key issue in hyperspectral data exploitation is to extract the spectra of the constituent materials (endmembers) as well as their proportions (fractional abundances) from each measured spectrum of mixed pixel in hyperspectral remote sensing image, called spectral un-mixing. Linear spectral mixture model (LSMM) provides an effective analytical model for spectral unmixing, which assumes that there is a linear relationship among the fractional abundances of the substances within a mixed pixel. To be physically meaningful, LSMM is subject to two constraints: the first constraint requires all abundances to be nonnegative and the second one requires all abundances to be summed to one. Independent component analysis (ICA) has been proposed as an advanced tool to un-mix hyperspectral image. However, ICA is based on the assumption of mutually independent sources, which violates the constraint conditions in LSMM.