Spectral unmixing based on improved extended support vector machines

Extended support vector machines (ESVM) was introduced recently for spectral unmixing. It models a class using a group of representative spectra to accommodate within class spectral variation. This paper presents a further geometry analysis of this method, and an improved ESVM is developed, which takes into account both within-class spectral variability and within each mixed case. The experiments illustrate that the new proposed algorithm can obtain more realistic unmixing results.