Oil Spill Segmentation using Deep Encoder-Decoder models
This work addresses the challenge of remote oil spill detection for environmental monitoring, but it is incremental as it compares existing segmentation models on specific data.
The authors tackled the problem of detecting oil spills by testing deep encoder-decoder models on satellite SAR image data, with the best model achieving a mean IoU of 64.868% and an improved class IoU of 61.549% for oil spills compared to a previous benchmark.
Crude oil is an integral component of the world economy and transportation sectors. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unfortunate yet unavoidable. Even though oil spills are difficult to clean up, the first and foremost challenge is to detect them. In this research, the authors test the feasibility of deep encoder-decoder models that can be trained effectively to detect oil spills remotely. The work examines and compares the results from several segmentation models on high dimensional satellite Synthetic Aperture Radar (SAR) image data to pave the way for further in-depth research. Multiple combinations of models are used to run the experiments. The best-performing model is the one with the ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over Union (IoU) of 64.868% and an improved class IoU of 61.549% for the ``oil spill" class when compared with the previous benchmark model, which achieved a mean IoU of 65.05% and a class IoU of 53.38% for the ``oil spill" class.