Structured neural networks for the classification of multisensor remote-sensing images

Proposes the application of structured neural networks to the supervised classification of multisensor remote-sensing images. The purpose of the proposed approach is to exploit neural networks advantages while solving, in the context of the considered application, the problems of “architecture definition” and of “opacity”. The architecture of the proposed neural networks reflects the provenance of data from different sensors. This allows one to easily define a network architecture by exploiting the characteristics of a given multisensor classification problem. In addition, the “structuring” of the architecture notably helps to understand the classification criteria implemented by the neural network classifier. To make possible such an interpretation, a transformation of the representation of original networks into a “simplified representation” has also been defined. The advantages provided by such networks are pointed out from the viewpoint of the remote-sensing application. Experimental results on multisensor data and comparisons with the Bayesian classifier are reported