DAENet: A Deep Attention-Enhanced Network for Cropland Extraction in Complex Terrain from High-Resolution Satellite Imagery

Yushen Wang, Mingchao Yang, Tianxiang Zhang, Shasha Hu and Qingwei Zhuang ()
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Yushen Wang: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Mingchao Yang: China Coal Zhejiang Surveying and Mapping Geo-Information Co., Ltd., Hangzhou 311000, China
Tianxiang Zhang: Zhejiang Zhixing Surveying and Mapping Geographic Information Co., Ltd., Hangzhou 311199, China
Shasha Hu: Key Laboratory of Jiang Huai Arable Land Resources Protection and Eco-Restoration, No. 302 Fanhua Avenue, Hefei 230088, China
Qingwei Zhuang: Key Laboratory of Jiang Huai Arable Land Resources Protection and Eco-Restoration, No. 302 Fanhua Avenue, Hefei 230088, China

Agriculture, 2025, vol. 15, issue 12, 1-22

Abstract: Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented plots, pronounced elevation differences, and non-uniform field borders. To address these challenges, we propose DAENet, a novel deep learning framework designed for accurate cropland extraction from high-resolution GaoFen-1 (GF-1) satellite imagery. DAENet employs a novel Geometric-Optimized and Boundary-Restrained (GOBR) Block, which combines channel attention, multi-scale spatial attention, and boundary supervision mechanisms to effectively mitigate challenges arising from disjointed cropland parcels, topography-cast shadows, and indistinct edges. We conducted comparative experiments using 8 mainstream semantic segmentation models. The results demonstrate that DAENet achieves superior performance, with an Intersection over Union (IoU) of 0.9636, representing a 4% improvement over the best-performing baseline, and an F1-score of 0.9811, marking a 2% increase. Ablation analysis further validated the indispensable contribution of GOBR modules in improving segmentation precision. Using our approach, we successfully extracted 25,556.98 hectares of cropland within the study area, encompassing a total of 67,850 individual blocks. Additionally, the proposed method exhibits robust generalization across varying spatial resolutions, underscoring its effectiveness as a high-accuracy solution for agricultural monitoring and sustainable land management in complex terrain.

Keywords: cropland extraction; complex terrain; geometric-optimized and boundary-restrained (GOBR) block; deep attention-enhanced net (DAENet); high-resolution imagery (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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