Investigation of Entropy Nets Induced by Oblique Decision Trees for Target Detection in Ocean SAR

Abstract

An artificial neural network whose topology is informed by an Oblique Decision Tree is applied to target detection in maritime Synthetic Aperture Radar. The number of neurons in the first layer is the same as the number of decision tree nodes and the number of nodes in the second hidden layer is the same as the number of leaf nodes. The neural network output are the class labels. Our approach differs from other efforts in the literature in that the Oblique Decision Tree and the Fisher´s Linear Discriminant are used as a decision criterion. Classifier testing and validation were achieved, applying these algorithms to radar images. Initial results are practical with satisfactory training time; generalization capability and a speedy architecture definition.