An efficient wavelet dictionary for texture separation

In this paper, our goal is to highlight the importance of the source separation method on remote sensing data analysis when dealing with urban areas characterized by spatial concept like texture. Source separation has become an attractive tool used to compensate physical information deficiency by statistical assumptions. The method´s key comes from the fact that the blind signal separation can be achieved by restoring statistical independence. In this work, we try to design a statistical generative model, based on a wavelet dictionary, composed of atoms which are automatically selected to maximize the sparseness of the underlying texture type. This application is of utmost importance in the classification process and should minimize the misclassification risk of urban areas.