Abstract
In recent years, researchers have paid a lot of attention to Layered Neural Networks (LNNs) as a non-parametric approach for the classification of remotely sensed images. This paper focuses on the generalization capability of LNNs, that is, how well an LNN performs with unknown data. First, we clarify its description from the point of view of information statistics. With this discussion, we provide a feasible technique to design the LNN in consideration of its generalization capability. Finally, we apply the proposed technique to a practical land cover classification using remotely sensed images, and demonstrate its potential.
