IMAGE FUSION AND SPECTRAL UNMIXING OF HYPERSPECTRAL IMAGES FOR SPATIAL IMPROVEMENT OF CLASSIFICATION MAPS

In this paper we propose a new approach for the improvement of the spatial resolution of hyperspectral image classification maps combining both spectral unmixing and pansharpening approaches. The main idea is to use a spectral unmixing algorithm based on neural networks to retrieve the abundances of the endmembers present in the scene, and then use the spatial information retrieved from the pansharpened image to find the location of each endmember within the enhanced pixel according to the endmembers abundances. The proposed approach has been applied both to real and synthetic datasets.