Geodaysit 2023

This study presents a novel approach to monitor oil spills and ships using Synthetic Aperture Radar (SAR) raw data and deep learning techniques. The proposed methodology involves several steps including pre-processing (focusing, filtering and land sea mask), semantic segmentation, and classification using a deep convolutional neural network (DCNN) model, as well as real-time (FFT-based) processing to ensure a fast response.

To train the DCNN model, the study combined three datasets: CleanSeaNet, TenGeoP-SARwv, and GAP_OilSpill_DB. The first two datasets are publicly available, while the third dataset was specifically built by the authors by integrating known and documented case studies from news articles and cases identified in the sea area in front of the port of Brindisi (Southern Italy), internally validated by expert GAP operators.

Data augmentation techniques were also utilized to improve the model's performance by generating additional training data. The DCNN model uses DeepLab v3+ based on ResNet-18 and is trained on a large dataset of SAR images that includes various types of oil spills, look-alikes, novelty objects, and ships.

The proposed system is optimized to process data on board the satellite to ensure a real-time response. The system transmits images to the ground segment only if there is an event of interest (e.g. a novelty object or an oil spill detected eventually involving the nearest ships).

The study demonstrates that the proposed approach provides a promising solution for real-time monitoring of oil spills, ships and novelty objects using satellite SAR raw data. The use of deep learning and data augmentation techniques can significantly improve the accuracy and speed of detection, which can ultimately lead to better environmental management and oil spill response. .Additionally, the proposed approach can be applied to a variety of SAR datasets and has the potential to be integrated with existing oil spill response systems.

Acknowledgments 

This work was carried out in the framework of the APP4AD project (“Advanced Payload data Processing for Autonomy & Decision”, Bando ASI “Tecnologie Abilitanti Trasversali”, Codice Unico di Progetto F95F21000020005), funded by the Italian Space Agency (ASI). ERS, ENVISAT and Sentinel-1 data are provided by the European Space Agency (ESA).