S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data

作者：Junyang Xie, Mengyao Zhang, Hao Wu, Anqi Lin, Marcos Adami, Abdul Rashid Mohamed Shariff, Yahui Guo · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2025 · DOI：10.1016/j.jag.2025.104505 · 被引用次数：9 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Image Processing and 3D Reconstruction

• S2-IFNet, a novel spatial-semantic fusion network, is proposed for extracting forest land. • A boundary feature enhanced spatial-semantic fusion strategy is integrated into S2-IFNet. • S2-IFNet outperforms existing forest land-cover products in accuracy and robustness. • The performance of S2-IFNet is validated across different climate zones worldwide. Accurately extracting forest land and understanding its spatial distribution are crucial for forest monitoring and management. However, variations in tree species, human activities, and natural disturbances create diverse and distinct forest land characteristics in remote sensing images, posing challenges for precise forest land extraction. To address these challenges, we propose a spatial-semantic information fusion network (S2-IFNet) integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data. S2-IFNet employs a dual-branch network to separately extract the spatial and semantic features of forest land. In the semantic branch, two modules are introduced: a boundary enhancement module using the Sobel operator to capture forest land boundary details, and an attention module to strengthen the feature representation capability. Finally, a spatial-semantic fusion module effectively combines the spatial, semantic, and boundary detail information to improve the forest land extraction accuracy. S2-IFNet was evaluated across five regions in different global climate zones, with a comparative analysis c...