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Radar Image Segmentation using Second-Order Recurrent Networks
University of Skövde, School of Humanities and Informatics.
1996 (English)Report (Other academic)
Abstract [en]

A second-order recurrent artificial neural network architecture for the segmentation and integration of radar images is introduced in this paper. This architecture consists 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 this mechanism allows networks to learn to solve the task through a dynamic adaptation of their weighting of different radar measurements.behaviour.

Abstract [en]

Annotation: In Bulsari, Kallio & Tsaptinos (eds.) Solving Engineering Problems with Neural Networks - Proceedings of the International Conference EANN'96, Systems Engineering Association, Finland

Place, publisher, year, edition, pages
Skövde: Institutionen för kommunikation och information , 1996.
Series
IKI Technical Reports, HS-IDA-TR-96-008
Keyword [en]
classification, radar image segmentation, recurrent artificialneural networks
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-1237OAI: oai:DiVA.org:his-1237DiVA: diva2:2370
Available from: 2008-06-17 Created: 2008-06-17 Last updated: 2010-03-24

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
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Output format
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