Parametric versus discriminative learning approach for multispectral image recognition

Land cover classification for remote sensing data have motivated several researches such us source separation, feature extraction and classification method. In this work, we aim to provide enhanced pattern recognition method based on source separation. Then, new data presentation is processed by feature extraction and adaptative classification. The non linearity for the mixing phenomenon is approximated by neural network. Adaptative classification approach is based on a parametric feature characterization. We will compare this approach to a discriminative classifier such as Support vector machine. Experimentations are based on SPOT4 observations. They demonstrates that the new presentation and the parametric model allows more efficient pattern identification. The results prove the potential of the method for urban areas.