Towards an intelligent multi-sensor satellite image analysis based on blind source separation using multi-source image fusion

Abstract

In this paper we propose a new approach for land cover classification using blind sources separation (BSS) and satellite image fusion methods simultaneously. Satellite image pixels are represented by radiometric values where each pixel is considered as a mixture of several independent sources. The BSS methods were developed in order to extract maximum information from different statistical features such as spatial correlation and local high order statistics. The statistical independence of the sources can be obtained through the joint approximate diagonalization of eigen-matrix in two dimensions (JADE-2D) algorithm. A reduction of spatial correlation can be obtained using the second order blind identification in two dimensions (SOBI-2D) algorithm. Non-Gaussianity can be measured using the fast-independent component analysis in two dimensions (Fast-ICA-2D) algorithm. These algorithms allow extraction of features by estimating the source images, mixing and un-mixing the matrix. These source images will be used by our framework as secondary knowledge, which is useful for a supervised classification.