Radar image segmentation using self-adapting recurrent networks.

T Ziemke

doi:10.1142/s0129065797000070

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PubMed · 9228576

Radar image segmentation using self-adapting recurrent networks.

T Ziemke

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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 sub-networks: 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.

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Doppler Effect Environmental Monitoring Image Processing, Computer-Assisted Learning Neural Networks, Computer Radar Systems Integration Journal Article Research Support, Non-U.S. Gov't

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T Ziemke. 1997. Radar image segmentation using self-adapting recurrent networks.. https://doi.org/10.1142/s0129065797000070

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