Marine oil spill detection and segmentation in SAR data with two steps Deep Learning framework.

Marine oil spills pose significant ecological and economic threats worldwide, requiring effective decision-making tools. In this study, the optimal parameters, and configurations for Deep Learning models in oil spill classification and segmentation using Sentinel-1 SAR imagery were identified. First, a new Sentinel-1 image dataset was created.

Oil spills: Detection and concentration estimation in satellite imagery, a machine learning approach.

The method's development to detect oil-spills, and concentration monitoring of marine environments, are essential in emergency response. To develop a classification model, this work was based on the spectral response of surfaces using reflectance data, and machine learning (ML) techniques, with the objective of detecting oil in Landsat imagery. Additionally, different concentration oil data were used to obtain a concentration-estimation model.