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Radar Image Segmentation using Self-Adapting Recurrent Networks
University of Skövde, School of Humanities and Informatics.
1997 (English)Report (Other academic)
Abstract [en]

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.

Abstract [en]

Annotation: In International Journal of Neural Systems, 8(1), 47-54.

Place, publisher, year, edition, pages
Skövde: Institutionen för kommunikation och information , 1997. , 8 p.
Series
IKI Technical Reports, HS-IDA-TR-97-002
Keyword [en]
radar image segmentation, recurrent artificial neural networks, second-order networks, self-adaptation, target classification
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-1238OAI: oai:DiVA.org:his-1238DiVA: diva2:2371
Available from: 2008-06-17 Created: 2008-06-17 Last updated: 2010-03-24

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Citation style
  • apa
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