A new classification approach based on source separation and feature extraction

Pattern recognition for multispectral data aims to identify land cover thematics for environmental monitoring and disaster risk reduction. Multispectral images contain data acquired from different channels within the frequency spectrum. They represent a mixture of latent signals. This paper represents a pattern recognition contribution for remote sensing. We propose a new classification framework based on nonlinear source separation and linear feature fusion. The first stage performs a nonlinear separation model based on multilayer neuron network. The underlying sources are Gaussians and a misfit function between the approximated source distributions and their prior's will be minimized iteratively. The second stage performs feature extraction and fusion. The linear feature model considers that feature descriptors allow cooperative description for land pattern recognition. Classification tasks are performed by Support Vector Machine. Experimentation results demonstrate that the proposed classification method enhances the recognition accuracy and provides a powerful tool for land identification.