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

This paper discusses the application of artificial neural networks to the segmentation of Doppler radar images, in particular the detection of oil spills within sea environments, based on a classification of radar backscatter signals. Best results have been achieved with recurrent backpropagation networks of an architecture similar to that of Elman's Simple Recurrent Network. The recurrent networks are shown to be very robust to variations in both sea state (weather conditions) as well as illumination distance, and their performance is analysed in further detail.

Abstract [en]

Annotation: In Pattern Recognition Letters, 17, 319-334, special issue 'Computer Vision Applications of Artificial Neural Networks', Elsevier Science, May 1996.

Place, publisher, year, edition, pages
Skövde: Institutionen för kommunikation och information , 1996. , 16 p.
Series
IKI Technical Reports, HS-IDA-TR-96-001
Keyword [en]
oil spill detection, radar image segmentation, recurrent artificial neural networks
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-1236OAI: oai:DiVA.org:his-1236DiVA: diva2:2369
Available from: 2008-06-17 Created: 2008-06-17 Last updated: 2010-03-24

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  • apa
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