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Radar Image Segment...
Radar Image Segmentation using Recurrent Artificial Neural Networks
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- Ziemke, Tom (author)
- Högskolan i Skövde,Institutionen för datavetenskap,The Connectionist Research Group
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(creator_code:org_t)
- Skövde : University of Skövde, 1996
- English 16 s.
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Series: IDA Technical Reports ; HS-IDA-TR-96-001
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Abstract
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- 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.
- HS-IDA-TR-96-001Annotation: In Pattern Recognition Letters, 17, 319-334, special issue 'Computer Vision Applications of Artificial Neural Networks', Elsevier Science, May 1996.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
Keyword
- oil spill detection
- radar image segmentation
- recurrent artificial neural networks
- Computer and systems science
- Data- och systemvetenskap
Publication and Content Type
- vet (subject category)
- rap (subject category)
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