Multispectral satellite image analysis based on the method of blind separation and fusion of sources

The number of satellite images is in full evolution allowing us to improve the extraction of useful information related to the physical reality of natural scenes. In this paper, we propose a new hybrid approach of multi-spectral analysis of satellite images. This approach consists in a method of blind separation of sources. This method allows us to decompose a pixel into information coming from independent sources. Algorithms adapted in the context of our work, operating in the two dimensional space, have been used for the separation. These algorithms produces many sources, in order to choose among them the most significant having a maximum value of entropy representing a maximum information about one class of land use. In order to have a classified image with good classification, we proceed with the fusion of these sources using a technique of maximum likelihood classification. We validated our approach on optical images of the satellite SPOT 4 and radar images of the satellite ERS 2 representing a central Tunisian region. The results obtained consists in the production of five classes of land use.