Further results on AMM for endmember induction

Our main interest is to perform unsupervised segmentation of the hyperspectral images. Our approach is to interpret abundance images resulting from spectral unmixing as the characterization of regions in the image. We induce the endmembers needed for spectral unmixing from the image data. Therefore the endmember spectra are not easily interpretable as laboratory spectra. Our method for endmember induction looks at the morphological independence or the endmembers as a necessary condition. We use the Autoassociative Morphological Memories (AMM) as detectors of morphological independence conditions. Our algorithm needs only one pass of the image. The experimental results obtained over a set of synthetic images are presented here, contrasted with the ICA and CCA approaches.