CT-HiffNet: A contour-texture hierarchical feature fusion network for cropland field parcel extraction from high-resolution remote sensing images

作者：Hao Wu, Junyang Xie, Weihao Deng, Anqi Lin, Abdul Rashid Mohamed Shariff, Shamshodbek Akmalov, Wenbin Wu, Zhaoliang Li, Qiangyi Yu, Qunming Wang, Jian Zhang, Xin Mei, Qiong Hu · 发表于：Computers and Electronics in Agriculture · 年份：2025 · DOI：10.1016/j.compag.2025.111010 · 被引用次数：27 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Remote Sensing and LiDAR Applications

• CT-HiffNet, a novel feature fusion network, was proposed for extracting cropland field parcels. • Contour–texture feature attention and guidance modules were integrated into CT-HiffNet. • CT-HiffNet has proven highly adaptive to different sensor types and image resolutions. • Both generalization and transferability of CT-HiffNet were validated on a global scale. Automatically extracting cropland field parcels from remote sensing images is crucial for developing smart agriculture. However, notable spatio-spectral differences captured by multiple remote sensing sensors at different times led to the uncertain contour and texture features among large-scale cropland field parcel, posing challenges for robust and high-precision extraction. To address these challenges, we proposed a contour-texture hierarchical feature fusion network (CT-HiffNet) for cropland field parcels extraction from high-resolution remote sensing images. The CT-HiffNet consists of three modules: a hybrid module integrating attention and guidance method to thoroughly learn the internal texture features as well as external contour features of cropland field parcels; a deep residual shrinkage block for feature encoding to effectively eliminate redundant information during the extraction tasks; and a hierarchical information fusion decoder to enhance contour-texture feature interactions at different scales and minimize information loss during feature restoration. The CT-HiffNet was evaluated across four distinct...