DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation - Liang Huang; Zixuan Zhang; You Yu; Bo-Hui Tang (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025)
To address the challenge of low automation accuracy in cropland extraction caused by complex mountainous terrain, severe cropland fragmentation, and ambiguous boundaries, this study proposes a novel semantic segmentation model for cropland in high-resolution remote sensing imagery, termed detail-enhanced and distance-attenuated network (DEDANet), which integrates detail enhancement and distance attenuation mechanisms. Into the encoding stage, introduced is a detail enhancement convolution module that amplifies high-frequency edge information; through a multibranch feature extraction pathway fusing five distinct convolutional types, the model significantly enhances its sensitivity and representational capacity for irregular cropland boundaries. Particularly in the decoder stage, embedded is a distance-attenuated transformer module, wherein an attenuation matrix assigns differentiated attention weights to pixels across spatial locations—thereby suppressing irrelevant background interference and reinforcing the contextual coherence of adjacent regions. By jointly optimizing and integrating global dependencies with local fine-grained details, not only are long-range contextual relationships captured, but also the precision of localized boundaries is substantially refined, leading to notable improvements in cropland boundary delineation and internal connectivity under complex terrains. To evaluate the model’s performance, a mountainous cropland dataset encompassing diverse cropland types was constructed. Experimental results demonstrate that the DEDANet surpasses mainstream models across key metrics, achieving an overall accuracy of 95.76%, an F1-score of 95.15%, and a mean intersection over union of 90.79%. Furthermore, ablation studies and cross-regional validations on the high-resolution cropland non-agriculturalization and iFLYTEK datasets verify the model’s robust generalization ability, thereby offering an efficient and scalable technical solution for refined cropland management in mountainous regions.