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Radar Image Segmentation using Self-Adapting Recurrent Networks

By Tom Ziemke Connectionist and Tom Ziemke

Abstract

This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two subnetworks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context. Keywords: radar image segmentation, recurrent artificial neural networks, second-order networks, self-adaptation, target classification 2 1 Introduction The work presented in this paper is concerned with the application of artificial neural networks (..

Topics: radar image segmentation, recurrent artificial neural networks, second-order networks, self-adaptation, target classification
Publisher: World
OAI identifier: oai:CiteSeerX.psu:10.1.1.31.4879
Provided by: CiteSeerX
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