摘要
【目的】精细提取全球地表水对水资源管理及气候监测具有重要意义。合成孔径雷达(SAR)凭借主动微波成像技术,可面向多云多雨地区实现全天时、全天候水体动态变化监测。针对传统方法对河网密布、地形复杂地区水体提取精度低以及由于SAR成像特性导致难以精准区分水体、山体阴影、裸地等难题,本文提出了一种顾及SAR多通道信息的多尺度特征融合水体提取MSFSwin(Multi-Scale Feature Fusion Swin)网络模型。【方法】通过融合升、降轨Sentinel-1影像的双极化特征及数字高程模型(DEM)构建多通道遥感水体数据集;并引入空洞空间金字塔池化(ASPP)模块,通过不同感受野的特征聚合增强对多尺度水体区域的感知能力,弥补Swin Transformer在跨窗口特征整合方面的不足;同时,设计多层次解码结构,通过层级特征融合强化跨尺度信息交互,实现水体精细提取。为验证本文提出MSFSwin模型的有效性与鲁棒性,选取青藏高原典型水体覆盖区进行水体提取实验,并定性、定量比较不同水体提取方法(含多种深度学习模型及KNN算法)的精度。【结果】MSFSwin模型在水体提取精度上明显优于Swin Transformer、Segformer、ViT等模型及KNN算法。其中,MSFSwin模型在干流湖泊交汇区的BF-score较原模型Swin Transformer提升了4.15%,在细小水体区IoU提升了3.52%,实现了复杂地形下的水体高精度自动提取。【结论】本文通过MSFSwin模型及多通道融合策略对水体、山体阴影等区分效果明显,有效提升了模型在复杂地形条件下水体提取的鲁棒性与适用性,为全天时、全天候、高精度水体提取提供了可靠技术支持。
[Objectives]Accurate extraction of global surface water bodies is crucial for water resource management and climate monitoring.Synthetic Aperture Radar(SAR),with its active microwave imaging capabilities,enables all-weather,all-day monitoring of water body changes in areas with persistent cloud cover and heavy rainfall.However,the dense river networks and complex terrain of the Qinghai-Tibet Plateau,along with the imaging characteristics of SAR,pose significant challenges that often hinder the accurate differentiation of mountain shadows,bare land,and water bodies.To address these challenges,this paper proposes a Multi-Scale Feature Fusion Water Body Extraction model,MSFSwin(Multi-Scale Feature Fusion Swin),which leverages the multi-channel information of SAR.[Methods]By integrating dual-polarization features from ascending and descending Sentinel-1 images with Digital Elevation Model(DEM)data,we construct a multi-channel remote sensing water dataset.To enhance the perception of multi-scale water bodies and compensate for the Swin Transformer's limitations in cross-window feature integration,we introduce an Atrous Spatial Pyramid Pooling(ASPP)module that aggregates features with different receptive fields.Additionally,a multi-level decoding structure is designed to strengthen cross-scale information interaction through hierarchical feature fusion,enabling refined water extraction.To validate the effectiveness and robustness of the MSFSwin model,experiments were conducted in typical water-covered regions of the Qinghai-Tibet Plateau.We performed both qualitative and quantitative comparisons against several deep learning models(e.g.,Swin Transformer,Segformer,ViT)and the KNN algorithm.[Results]The experimental results show that the MSFSwin model outperforms Swin Transformer,Segformer,and ViT in water body extraction.In the river-lake confluence area,the BF-score of the proposed method improved by 4.15%compared to the baseline model,while the IoU in areas with small water bodies increased by 3.52%,enabling high-precision automatic water extraction in complex terrains.[Conclusions]By leveraging the MSFSwin model and a multi-channel fusion strategy,this study achieves clear distinction between water bodies and mountain shadows,substantially improving the robustness and adaptability of water body extraction in complex terrains.The proposed approach offers a reliable solution for high-precision,all-weather,all-day water body monitoring.Code Link:https://github.com/infinitas732/code.
作者
王雷
刘文宋
张连蓬
李二珠
郭风成
路琦
WANG Lei;LIU Wensong;ZHANG Lianpeng;LI Erzhu;GUO Fengcheng;LU Qi(School of Geography,Geomatics and Planning,Jiangsu Normal University,Xuzhou 221116,China)
出处
《地球信息科学学报》
北大核心
2025年第7期1638-1655,共18页
Journal of Geo-information Science
基金
国家自然科学基金青年科学基金项目(62201232)
江苏省研究生科研与实践创新计划项目(KYCX25_3182)
南京北斗创新应用科技研究院开放基金项目(20230502)
徐州市社会事业项目(KC23304)。
