A Novel Boundary Enhancement Network for Surface Water Mapping Based on Sentinel-2 MSI Data

Abstract

Accurate surface water mapping is crucial for monitoring and protecting ecosystem environments. During the past decades, several water extraction methods have been presented and significant progress has been made. However, most of the existing research has mainly focused on reducing the interference factors, such as clouds and cloud shadows, building shadows, and mountain shadows. There are relatively few studies concentrated on the fine extraction of water body boundaries, which is of equal importance for surface water mapping. Therefore, in this article, we developed a novel boundary enhancement network, to improve its ability to extract water boundary information. The proposed BE-Net consists of three modules, i.e., SE, SA, and MFF, which are used to focus on water features, water boundary information, and fuse features of different levels, respectively. In addition, the Sobel edge loss function is adopted. The proposed model was tested on six regions with significant differences in water body morphology and the contribution of the Sobel edge loss function and the three modules were investigated. The results demonstrated that: 1) compared with the state-of-the-art methods, the proposed BE-Net achieved the best accuracy in all testing areas and was all above 97%; 2) Compared with the commonly used loss function, the Sobel edge loss function can better capture the detail boundary information, resulting in higher accuracy; and 3) by integrating the SE, SA, and the MFF modules, a robust boundary enhancement network is constructed, and the water extraction accuracy can be significantly improved.

Keywords

• Boundary enhancement
• deep learning
• loss function
• surface water mapping