Automatic Recognition of Oil Spills Using Neural Networks and Classic Image Processing

نویسندگان

چکیده

Oil spill events are one of the major risks to marine and coastal ecosystems and, therefore, early detection is crucial for minimizing environmental contamination. have a unique appearance in satellite images created by Synthetic Aperture Radar (SAR) technology, because they byproducts oil’s influence on surface capillary, causing short gravity waves that change radar’s backscatter intensity result dark formations SAR images. This signature’s can be utilized monitor automatically detect oil spills Although sensors capture these formations, which likely connected spills, it hard distinguish them from ships, ocean, land, other oil-like formations. Most approaches automatic classification employ semantic segmentation with convolutional neural networks (CNNs), using custom-made dataset. However, struggle between spots resemble them. Therefore, developing tailor-made sequence methods recognition challenge an essential need, should include examination choice most effective preprocessing tools, CNN models, datasets specifically challenge. paper suggests new accurate detection. First, image filtering technique was used emphasizing physical characteristics spills. Each filter’s impact leading architectures performances examined. Then, method model ensemble used, aiming reduce generalization error. All experiments demonstrated this confirm suggested, comparison common formula, leads 4.2% improvement intersection over union score (IoU) detection, 9.3% mean IoU among several relevant classes.

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ژورنال

عنوان ژورنال: Water

سال: 2022

ISSN: ['2073-4441']

DOI: https://doi.org/10.3390/w14071127