The contribution of the sources separation method in the decomposition of mixed pixels

In this paper, we propose to prove the importance of the application of blind sources separation methods on remote sensing data. Indeed, satellite images are represented by radiometric values where each one is considered as a mixture of different sources. The primary goal of our research is to hand back the different sources covering the scanned zone. The main constraint to restore these sources is to take our observation images as a mixture of physically independent components. In our work, the independence between the different sources is obtained through two statistical methods. The first method is based on the reduction of the spatial source correlations, and the second one is based on the joint maximization of the fourth-order cumulants. On the opposite of the original multispectral images that are represented according to correlated axes, the source images extracted from the proposed algorithms are represented according to mutually independent axes that allow each source to represent specifically a certain type of land cover. This increases the reliability of the analysis and the interpretation of the scanned zone. The source images obtained from the application of the sources separation method give a more effective representation of the information contained on the observation images. The performance of these source images is investigated through an application for the decomposition of mixed pixels. The originality of our application comes from the determination of the mixing matrix modeling the spectral endmembers based on source filters. These filters model the sensibility of each source channel according to the different spectral bands, which give an interesting information about the spectral theme represented by the corresponding source image. This application shows that the proportions of the different land cover types existing into the pixel are better estimated through the source images than through the original multispectral images. This method could offer an interesting solution to mixed-pixel classification.