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A Texture Analysis And Neural Network Method For The Classification Of SAR Images With Spilled Oil

Posted on:2006-03-10Degree:MasterType:Thesis
Country:ChinaCandidate:L S ZhuFull Text:PDF
GTID:2168360155464898Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
Synthetic Aperture Radar (SAR) images are extensively used for the determination of oil spill in the marine environment, most of study found that oil spill appeared as dark slick or dark spot in SAR image due to the dampening effects on the sea. Utilizing the characteristics of abundant texture information in SAR image, this paper investigates a texture analysis and Neural Network method for the classification of SAR images with spilled oil, with particular emphasis on its applicability to get the accurate result of classification. In the progress of texture analysis, four pixel texture parameters that are sensitive to SAR image of oil spill are calculated by gray level difference statistics. The SAR image is classified by using the feature vector that is composed of the Gray Level Co-occurrence matrix features and gray of pixel. We use three types of algorithm: Back-Propagation Neural Network (BPNN), Radial Basis Function Network (RBFNN) and Probabilistic Neural Network (PNN). Using the data in form of 5-level feature vector as inputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. All of them have a good performance of classification; but PNN is the most effective and accurate one as classifier for SAR images of oil-spill.
Keywords/Search Tags:Synthetic Aperture Radar (SAR), Texture Analysis, Back-Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Probabilistic Neural Network (PNN)
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