Method for inland water body extraction fused atrous spatial pyramid pooling

Abstract

Water body extraction is a significant direction of application in information monitoring of water resources. Thus, on the basis of high-resolution UAV remote sensing images and self-build inland water body datasets, this paper designs a water body extraction method fused atrous spatial pyramid pooling. First, SegFormer detection network is improved, and the semantic segmentation network is adopted to mine water body semantic information of high-resolution UAV remote sensing images; second, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information, solve the problems such as feature information loss of water body and deficiency of long-distance information, and come true the coverage of more geometric information whilst responding to semantic features; finally, the semantic segmentation dataset of the inland water body is self-established on the basis of the UAV remote sensing images in Dengzhou, Henan, and the experiment is conducted. Then the validity of the proposed water body extraction method is validated via the comparative ablation experiment with Hrnet, PSPNet, UNet, Deeplabv3+ and the original detection algorithm. According to experimental results, the improved method is superior to the comparative detection methods such as UNet, and has a better effect on the integrity detection of water body edges and the reduction of missing detection and false detection of small water bodies and tributaries.