Second-Order Separation by Frequency-Decomposition of Hyperspectral Data

In this paper, we consider the problem of b lind image separation by taking advantage of the sparse representation of the hyperspectral images in the DCT-domain. Blind Source Separation (BSS) is an important field of research in signal and image processing. These images are produced by sensors which provide hundreds of narrow and adjacent spectral bands. The idea behind t ransform do main is that we can restructure the signal/image values to give transform coefficients more easily to separate. This work describes a novel approach based on Second-Order Separat ion by Frequency-Decomposition, termed SOSFD. This technique uses joint information fro m second-order statistics and sparseness decomposition. Furthermore, the proposed approach has the added advantages of the DCT and second-order statistics in order to select the optimu m data informat ion. In fact, representing the hyperspectral images in well suited database functions allows a good distinction of various types of objects. Results show the contribution of this new approach for the hyperspectral image analysis and prove the performance of the SOSFD algorith m for hyperspectral image classification. On the opposite of the original images that are represented according to correlated axes, the source images extracted fro m the proposed approach are represented according to mutually independent axes that allow a more efficient representation of information contained in each image. Then, each source can represent specifically certain themes by exploit ing the link between the frequency-distribution and structural composition of the image. Th is application is of utmost importance in the classification process and could increase the reliability of the analysis and the interpretation of the hyperspectral images.