Abstract: Oil spills, which may be generated by various sources and can enter the ocean via various entry sites, are a significant pollutant that significantly influences marine ecosystems and can have far-reaching and disastrous consequences. The health of marine and coastal ecosystems is severely threatened by oil spills, regardless of whether they result from an accident or ships cleaning their tanks. Satellite synthetic Aperture Radar (SAR) devices have a high degree of accuracy in identifying oil spills. These systems function regardless of cloud cover or sunlight and can differentiate oil from a stable sea surface. We present the OSD-DNN framework for monitoring the seas for signs of oil leakage. A convolutional neural network (CNN) with improved SPP Net architecture was employed for training and Testing during the analysis of the proposed method. After that, an improved cross-entropy Adam optimization is used on the model compilation. A strategy that employs Non-adaptive thresholds was applied to image denoising. The Contrast-limited adaptive histogram equalization (CLA.HE) method was used to normalize the histograms. The median threshold canny approach is used for the process of image segmentation. The CNN method was used for the process of feature extraction. The deep convolutional neural network (DCNN) method can see and identify oil spills. According to the experiment's findings, using a deep neural network to detect oil spills improved accuracy, and the Deep CNN performed better than the existing methods.

Key words: DCNN, oil spill, sppnet, segmentation, OSD-DNN rock burst, big data, visualization, online learning algorithm