Daytime sea fog monitoring using multimodal self-supervised learning with band attention mechanism.

Sea fog is a dangerous weather phenomenon that seriously affects maritime traffic and other operations at sea. The conventional sea fog detection methods are not only difficult to make full advantage of the multispectral information of cloud images, but also deficient in the exploration of deep-level semantic information, leading to poor detection results. In this paper, we proposed a multimodal self-supervised convolutional neural network incorporating intra-modal band attention mechanism (MSCNN-IBAM) based on multispectral images of Himawari-8. MSCNN-IBAM uses independent branches to extract features from different modality cloud images and characterize the importance of each band through attention mechanisms. Simultaneously, multimodal self-supervised learning and supervised learning are effectively combined to optimize the model by constructing a two-tuple trainset. Experimental results show the accuracy, precision, recall, and F1 score of the proposed method as 97.72%, 95.84%, 96.54%, and 96.08%, respectively, which have the competitive performance and acceptable computational efficiency. And the additional analysis of sea fog cases shows that the proposed method is not only effective in identifying sea fog, but also has the ability to locate sea fog regions. [ABSTRACT FROM AUTHOR]