Satellite image analysis based on the method of blind separation of sources for the extraction of information

Presents the application of blind source separation (BSS) to satellite images produced with many sensors. These sensors measure the electromagnetic radiation emitted or reflected by the studied surface. Each pixel of these images represent a radiometric value that results from several objects or sources. So, a pixel is considered as a mixture of different sources. We propose to examine the ability of BSS methods to restore the independent sources by the use of algorithms based on high-order statistics such as the independent component analysis (ICA) and second order blind identification (SOBI). These techniques allow us to identify the images sources and the mixing and unmixing matrix in order to help the photointerpreter to improve his analysis of the satellite images. Each image source has maximum information about one source that can represent a class of land use. In order to have a classified image, we proceed with the fusion of these sources using a technique of maximum likelihood classification (MLC). The results obtained consist of five classes of land use (parcel region, urban region, humid region, cultivated region and sebkhat).