FEATURE-EXTRACTION FOR MULTISOURCE DATA CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORKS

Classification of multisource remote sensing and geographic data by ne ural networks is discussed with respect to feature extraction. Several feature extraction methods are reviewed, including principal componen t analysis, discriminant analysis, and the recently proposed decision boundary feature extraction method. The feature extraction methods are then applied in experiments in conjunction with classification by mul tilayer neural networks. The decision boundary feature extraction meth od shows excellent performance in the experiments.