Intelligent Recognition of Coastal Outfall Drainage Based on Sentinel-2/MSI Imagery

Abstract

In this study, we developed an innovative and self-supervised pretraining approach using Sentinel-2/MSI satellite imagery specifically designed for the intelligent identification of drainage at sea discharge outlets. By integrating the geographical information from remote sensing images into our proposed methodology, we surpassed the classification accuracy of conventional models, such as MoCo (momentum contrast) and BYOL (bootstrap your own latent). Using Sentinel-2/MSI remote sensing imagery, we developed our model through an unsupervised dataset comprising 25,600 images. The model was further refined using a supervised dataset composed of 1100 images. After supervised fine-tuning, the resulting framework yielded an adept model that was capable of classifying outfall drainage with an accuracy rate of 90.54%, facilitating extensive outfall monitoring. A series of ablation experiments affirmed the effectiveness of our enhancement of the training framework, showing a 10.81% improvement in accuracy compared to traditional models. Furthermore, the authenticity of the learned features was further validated using visualization techniques. This study contributes an efficient approach to large-scale monitoring of coastal outfalls, with implications for augmenting environmental protection measures and reducing manual inspection efforts.