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Recurrent Artficial Neural Networks for the Detection of Oil Spills from Doppler Radar Imagery
University of Skövde, Department of Computer Science. (The Connectionist Research Group)ORCID iD: 0000-0001-6883-2450
1995 (English)Report (Other academic)
Abstract [en]

This paper discusses the application of artificial neural networks (ANNs) to the detection of oil spills in sea clutter environments from the classification of radar backscatter signals. A comparison and evaluation of different network architectures regarding reliability of dection and robustness to varying sea states/wind conditions shows that for this problem best results are achieved with a recurrent architecture similar to that of Elman's SRN.

Place, publisher, year, edition, pages
Skövde: University of Skövde , 1995.
Series
IDA Technical Reports ; HS-IDA-TR-95-009
National Category
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-1239OAI: oai:DiVA.org:his-1239DiVA, id: diva2:2372
Note

HS-IDA-TR-95-009

Annotation: In Keating, John G. (ed.) Neural Computing - Research and Applications - Prodeedings of the Fifth Irish Neural Network Conference, St. Patrick's College, Maynooth, Co. Kildare, Ireland.

Available from: 2008-06-17 Created: 2008-06-17 Last updated: 2021-05-21Bibliographically approved

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Ziemke, Tom

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