A Semi-Supervised Model for Fine-Grained Identification of Oil Emulsions on the Sea Surface Using Hyperspectral Imaging

After oil spills occur in the ocean, oil pollutants usually appear in the form of oil emulsions under the influence of hydrodynamics. Hyperspectral remote sensing technology, which provides abundant spectral information of ground objects, has the potential of fine-grained classification on the types of oil emulsions. Aiming at the practical applications of oil emulsion extraction in hyperspectral images (HSIs), this study proposes a semi-supervised model for oil emulsion identification by integrating an image segmentation algorithm with a deep-learning-based classification model. In the proposed approach, the training data were filtered from HSI using an image segmentation algorithm, based on which a 1-dimensional convolutional neural network (1D-CNN) was trained to identify oil emulsions in the HSI. The model was tested on the HSIs of Deepwater Horizon oil spills obtained by AVIRIS. The overall accuracy and standard performance measurements of the proposed model are higher than 94% on the extracted dataset. The results indicated that the proposed model achieved similar detection results on sea water as the supervised model, and even higher accuracies on oil emulsion type identification. As a semi-supervised model, it also avoids the lengthy and time-consuming data labelling and has the potential for operational oil emulsions extraction and quantification.