Residual information to estimate uncertainty and improve the spectral linear mixing model solution

This paper proposes an analysis on the residual term resulting from the Linear Spectral Mixing Model (SLMM) solution in order to access model uncertainty. The framework employed here is based on analysis of data produced initially by unmixing of vegetation, bare soil and shade/water, whose are commonly used as standard endmembers. We suggest procedures to identify missing components in the mixture problem and automatically compute the spectral endmember values for these components directly from image data and residual information. The techniques proposed have been tested on real TM-Landsat. The results obtained promises and confirm the validity of the proposed approach.