Improving the results of spectral unmixing of Landsat thematic mapper imagery by enhancing the orthogonality of end-members

Spectral unmixing is a technique that has been developed to derive fractions of spectrally pure materials that contribute to observed spectral reflectance characteristics of a mixture through a inverse least-squares deconvolution using end-member spectra. This technique has been shown to be very successful when applied to high spectral resolution imaging or non-imaging data where subtle diagnostic absorption features largely determine the spectral characteristics of the data. A large and vastly growing number of papers where spectral unmixing is applied to analyse low resolution image data (e.g. Landsat Thematic Mapper (TM), NOAA AVHRR, etc.) often to derive abundances of different materials as input parameters for models (i.e. land degradation models, crop growth models, hydrologic models, etc.) has evolved throughout recent years. This justifies efforts put into the quality assessment of these abundance estimates. In this paper we evaluate the effect of end-member redundancy on the deconvolution of spec...