Self-supervised learning-based oil spill detection of hyperspectral images

Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions. However, previous studies mainly focus on the supervised detection technologies, which requires a large number of high-quality training set. To solve this problem, we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection, which consists of three parts: data augmentation, unsupervised deep feature learning, and oil spill detection network. First, the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model. Then, the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features. Finally, the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result, where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method. Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.