Self-Supervised Spectral–Spatial Transformer Network for Hyperspectral Oil Spill Mapping
Xudong Kang, Bin Deng, Puhong Duan, Xiaohui Wei, Shutao Li
Abstract
Hyperspectral oil spill mapping aims to distinguish the type of oil spill. Recently, most hyperspectral oil spill detection methods are based on supervised methods that work well with rich training samples. However, in the marine oil spill detection scenario, pixel annotations are difficult and costly. Moreover, the labels obtained by domain experts within a hyperspectral image (HSI) are often scarce. To address these issues, a self-supervised spectral-spatial transformer network is proposed for hyperspectral oil spill mapping. First, we propose a transformer-based contrastive learning network to extract the deep discriminative features. Then, the learned features are transferred to the downstream classification network that is fine-tuned with very few labeled samples. Experiments on hyperspectral oil spill database (HOSD) constructed by ourselves indicate that the proposed method can obtain more promising performance than several state-of-the-art oil spill classification techniques in discriminating different types of oil spills, i.e., thick oil, thin oil, sheen, and seawater.