A single individual evolutionary strategy for endmember search in hyperspectral images

We define an single individual evolutionary strategy (SIE) for the induction of a set of endmembers and we compare it with a conventional evolutionary strategy (ES), tailored to this task, over a couple of hyperspectral images. The SIE considers a set of end-members as an evolving population. Individuals correspond to hypothetical endmember spectra, and they are selected as candidates for mutation on the basis of their partial abundance images. The population's global fitness when the mutated individual substitutes its parent is the measure of the goodness of tile individual. Although the aim of defining the SIE was to reduce the computational cost of applying ES to the high volume data in hyperspectral images, we have found that SIE also improves the fitness performance of conventional ES.