Joint linear/nonlinear spectral unmixing of hyperspectral image data

Many available techniques for spectral mixture analysis involve the separation of mixed pixel spectra collected by imaging spectrometers into pure component (endmember) spectra, and the estimation of abundance values for each end member. Although linear mixing models generally provide a good abstraction of the mixing process, several naturally occurring situations exist where nonlinear models may provide the most accurate assessment of endmember abundance. In this paper, we propose a combined linear/nonlinear mixture model which makes use of linear mixture analysis to provide an initial model estimation, which is then thoroughly refined using a mufti-layer neural network coupled with intelligent algorithms for automatic selection of training samples. Three different algorithms for automatic selection of training samples, such as border train ing algorithm (BTA), mixed signature algorithm (MSA) and mophological erosion algorithm (MEA) are developed for this purpose. The proposed model is evaluated in the context of a real application which involves the use of hyperspectral data sets, collected by the Digital Airborne (DAIS 7915) and Reflective Optics System (ROSIS) imaging spectrometers of DLR, operating simultaneously at multiple spatial resolutions.