Chapter 7 - Case study I: Analysis of oil spill detection using deep neural networks

Abstract

Environmental pollution is one of the most crucial problems around the globe, where the oil spill has a significant impact on the environment. Oil spill detection at an early stage can save the environment, and especially the marine life greatly benefits from recovery systems. Synthetic aperture radars (SAR) are utilized to capture the images from the satellite, which is the primary and accurate source of the detection system. In SAR sensors, oil spills are captured as black spots where distinguishing between the real oil spill and the look-alike enables more challenging objectives. The research community has taken different approaches into account to detect and classify SAR black spots. However, most of them utilize a custom dataset, which makes the result unsuitable for comparison. The scenario worsens when only one label is assigned to the whole SAR image. Hence, deep convolutional neural networks (DCNNs) are suggested in the literature as a proficient method. Thus with SAR images, deep learning techniques can be utilized to detect the oil spill efficiently with other essential classes. This research work implements a DCNN architecture to identify oil spills and relevant classes (i.e., ship, land, look-alike) with a very recent and well-defined dataset for oil spill detection. The data set is preprocessed from raw SAR images collected from sentinel-1 European satellite images. In this work, FCN-8s architecture is utilized to identify oil spills and relevant classes with semantic segmentation techniques. The main objective of this study is to investigate the best appropriate setting of hyperparameters for oil spill detection. The evaluation results of this work reflect that the proposed DNN architecture performs best with the Adadelta optimizer for oil spill detection.