地理空间信息2026，Vol.24Issue(4)：75-79,5.DOI:10.3969/j.issn.1672-4623.2026.04.016
雷达-光学特征融合与自监督学习驱动的湿地动态监测
Wetland Dynamic Monitoring Driven by Radar-Optical Feature Fusion and Self-supervised Learning
摘要
Abstract
Addressing the challenges of cloud interference and multi-source data fusion in wetland monitoring in the Pearl River Delta,we pro-posed a spatio-temporal enhanced self-supervised attention network model(STE-SAN)integrating radar-optical spatio-temporal features.We inte-grated Sentinel-1 SAR and Landsat images to construct a spatio-temporal registered dataset,combined 3D convolution with bidirectional LSTM to extract multi-scale spatio-temporal features,and designed a polarization-sensitive attention mechanism to enhance the identification of wetland edge heterogeneity.The results show that the overall classification accuracy of STE-SAN reaches 91.7%(Kappa=0.88),which is 8.2 to 15.7 per-centage points higher than that of random forest and U-Net.It performs outstandingly in cloudy scenarios(F1=85.4%)and edge detection(IoU=0.81),and the annotation requirement is only 5%of that of traditional methods.The monitoring results indicated that from 2000 to 2020,the natural wetlands in the Pearl River Delta decreased by 15.9%(while the artificial wetlands increased by 111.5%),and 83%of the coastal wet-lands were encroached by aquaculture ponds,leading to the degradation of ecological functions.This model provides a high-precision and low-cost solution for wetland dynamic monitoring and conservation decision-making in highly urbanized areas.关键词
雷达-光学时空融合/自监督深度学习/时空特征增强型自监督网络模型Key words
radar-optical spatio-temporal fusion/self-supervised deep learning/STE-SAT分类
天文与地球科学引用本文复制引用
丁永祥..雷达-光学特征融合与自监督学习驱动的湿地动态监测[J].地理空间信息,2026,24(4):75-79,5.基金项目
广东省重点领域研发计划项目(232023021021900001). （232023021021900001）
