Does Independent Component Analysis Play a~Role in Unmixing Hyperspectral Data?

—Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2) sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. ...

• Type: Conference
• Year: 2003
• Where: IBPRIA
• Authors: José M. P. Nascimento, José M. B. Dias
• Keywords: Abundance Fractions, Hyperspectral, Hyperspectral Data, IBPRIA 2003, Image Analysis