Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
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The current study addresses the problem of the identification of each natural material present in each pixel of a hyperspectral image. Two end member extraction methods from hyperspectral imagery were studied: the N-FindR method and the independent component analysis (ICA). The N-FindR is an automatic technique that selects extreme points (end members) of an n-dimensional scatter plot. It assumes the existence of pure pixels in the distribution, which is infrequent in practice. ICA is a blind source separation technique studied in the signal processing community, which allows each spectrum of natural elements (end member spectra) to be extracted from the observation of some linear combinations of these. It considers a more realistic situation than N-FindR, assuming a spectra mixture for all the pixels. To increase the robustness of ICA, continuum-removed reflectance spectra were used and an iterative algorithm was introduced that takes into account a major part of the available information. The end member abundances were estimated by the fully constrained least squares spectral mixture analysis (FLCS). The end member identification and quantification were carried out on two surficial formations of a semi arid region located in the Rehoboth region, in Namibia, from hyperspectral Hyperion data. It appears that the two end member extraction methods have a similar potential. Whichever end member extraction method is used, the analysis of the rock abundance maps produces a lot of geological information: the distribution of natural elements is in line with the field observations and allows the description of the formation processes of surficial units.