摘要： 高分辨率遥感影像的高原湖泊水体精准识别对区域生态保护和水资源管理具有重要意义。针对高原场景中水体占比低、细节特征易丢失所引发的多尺度特征融合不充分与高频细节衰减，进而导致边界模糊、细小水体漏检及复杂场景误分割等难题，提出基于频域-空域协同的双分支多级融合网络(Wavelet-ResNet-Swin Network, WRS-Net)。通过自适应多级小波分解提取水体的低频轮廓与高频细节特征，同时采用多阶段ResNet50，在各阶段末通过高频门控单元增强纹理响应，捕获空间语义信息；继而设计跨模态注意力融合模块实现多尺度语义与细节的协同优化，结合特征对齐模块解决跨层级特征错位问题；最后通过Swin Transformer进行全局上下文建模。在自建高原湖泊数据集上的实验表明结果，WRS-Net的Acc和mIoU两个指标分别为96.52%和93.44%，优于其它对比网络，提升了对遥感影像中高原湖泊水体识别的精度。

Abstract: Accurate identification of water bodies in plateau lakes with high-resolution remote sensing images is of great significance for regional ecological protection and water resources management. Aiming at the insufficient multi-scale feature fusion and high-frequency detail attenuation caused by the low proportion of water bodies and easy loss of detailed features in the plateau scene, which leads to boundary blurring, omission of fine water bodies and mis-segmentation of complex scenes, we propose a two-branch multilevel fusion network based on the frequency domain-space domain synergy (Wavelet-ResNet-Swin Network (WRS-Net)). The low-frequency contour and high-frequency detail features of the water body are extracted by Adaptive Wavelet Decomposition, while a multi-stage ResNet50 is used to enhance the texture response by high-frequency gating units at the end of each stage to capture the spatial semantic information, Then the Cross Attention Fusion Module is designed to achieve the cooptimization of multi-scale semantics and details, combined with the Feature Alignment Module to solve the cross-layer feature misalignment problem; finally, the global context modeling is performed by Swin Transformer. Experiments on the self-constructed plateau lake dataset show that the Acc and mIoU metrics of WRS-Net are 96.52% and 93.44%, respectively, which are better than other comparative networks, and improve the accuracy of recognizing the water bodies of plateau lakes in remote sensing images.