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- W2770295513 abstract "ABSTRACTIdentification of oil spills from synthetic aperture radar (SAR) images is a complex task that has recently come into use. Existing oil spill detection methods have been found to be expensive and complex and require high processing power and time. Also, noise removal and extracting the features of oil-spill images are major issues. To overcome these drawbacks, a novel segmentation technique, adaptive edge and texture clustering (AETC), is proposed in this article to detect and classify the oil-spill area in a given image. The proposed technique contains four main stages: preprocessing, segmentation, feature extraction, and classification. Initially, the input image is preprocessed to eliminate speckle noise and to enhance the quality of the image by using the Gaussian distribution function. After that, the preprocessed image is segmented with the help of the linear edge weighted (LEW) clustering technique. In this stage, the boundary region is identified and the contour is segmented. The features ..." @default.
- W2770295513 created "2017-12-04" @default.
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- W2770295513 date "2017-10-02" @default.
- W2770295513 modified "2023-09-25" @default.
- W2770295513 title "AETC: Segmentation and classification of the oil spills from SAR imagery" @default.
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- W2770295513 doi "https://doi.org/10.1080/15275922.2017.1368044" @default.
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