SADA-Net: A Shape Feature Optimization and Multiscale Context Information-Based Water Body Extraction Method for High-Resolution Remote Sensing Images

Convolutional neural networks (CNNs) have significance in remote sensing image mapping, and pixel-level representation allows refined results. Due to inconsistencies within a class and different scales of water bodies, the water body mapping has challenges, such as insufficient integrity and rough shape segmentation. To resolve these issues, we proposed an intelligent water bodies extraction method (named SADA-Net) for high-resolution remote sensing images. This method considers multiscale information, context dependence, and shape features. The network framework integrates three critical components: shape feature optimization (SFO), atrous spatial pyramid pooling, and dual attention modules. SADA-Net can accurately extract an extensive range of water bodies in complex scenarios. SADA-Net has certain advantages regarding small and dense water bodies extraction, as the SFO module effectively solves the defects of the unified processing of low-level features in the encoder stage of CNNs, which highlights the shape information of a water body. Two data types (red, green, and blue bands and multispectral images) are employed to verify the performance of the proposed network. The best result achieved an evaluation index F1-Score of 96.14% in large-scale image segmentation, and the structural similarity index measure reached 94.70%. Overall, the proposed method achieves the purpose of maximizing the integrity and optimizing the shape of a water body. Additionally, the SADA-Net proposed in this article has a specific reference value for high-resolution remote sensing image water bodies mapping.