Paper Info
Title
SAR image processing using probabilistic winner-take-all learning and artificial neural networks
Abstract
This paper develops a two-stage approach for the identification of ship targets in airborne synthetic aperture radar (SAR) imagery representing open ocean scenes. The first stage of the developed approach segments the SAR image using a novel neural clustering scheme, called “probabilistic winner-take-all (PWTA)”. As for the second stage, it employs a backpropagation (BP) neural network to classify ships that may be found in the segmented SAR image. Experimental results are presented. These results demonstrate that the developed two-stage ship-identification approach is successful in automatically interpreting the SAR imagery even in the presence of confusing ships and natural clutter
Year
DOI
Venue
1996
10.1109/ICIP.1996.560945
Image Processing, 1996. Proceedings., International Conference
Keywords
Field
DocType
backpropagation,inference mechanisms,neural nets,radar applications,radar clutter,radar computing,radar imaging,radar target recognition,remote sensing by radar,ships,synthetic aperture radar,SAR image processing,SAR imagery,airborne synthetic aperture radar,artificial neural networks,backpropagation neural network,experimental results,natural clutter,neural clustering scheme,neural network learning,open ocean scenes,probabilistic winner-take-all,segmented SAR image,ship targets,target identification,two-stage ship-identification approach
Computer vision,Radar imaging,Pattern recognition,Computer science,Synthetic aperture radar,Image processing,Image segmentation,Artificial intelligence,Probabilistic logic,Winner-take-all,Backpropagation,Artificial neural network
Conference
Volume
ISBN
Citations 
1
0-7803-3259-8
2
PageRank 
References 
Authors
0.39
2
2
Name
Order
Citations
PageRank
Hossam Osman1152.46
Steven D. Blostein232961.46