Bibliographic Details
| Title: |
面向农业地块提取的边缘-语义协同双分支解码网络. |
| Alternate Title: |
Edge and semantic collaborative dual-branch decoding network for agricultural parcel extraction. |
| Authors: |
杨 梅1 yangmei@swpu.edu.cn, 刘司南1 202321000510@stu.swpu.edu.cn, 潘 臻2 zpan5@163.com, 高 磊1, 闵 帆1 |
| Source: |
Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Mar2026, Vol. 48 Issue 3, p444-455. 12p. |
| Subjects: |
Image segmentation, Edge detection (Image processing), Deep learning, Artificial neural networks, Attention, Remote sensing |
| Abstract (English): |
Accurate agricultural parcel extraction from remote sensing images for agricultural resource monitoring is a critical technology for achieving intelligent management of cultivated land resources. To address the insufficient segmentation accuracy caused by blurred boundaries, diverse textures, and morphological heterogeneity in complex farmland scenarios in existing deep learning methods, this paper proposes a multi-task neural network ESDNet featuring collaborative edge-semantic optimization. The model achieves performance improvements through three innovative mechanisms: Firstly, a coordinate attention (CA) module is embedded between the encoder and main decoder to enhance the discriminative capability for ambiguous boundaries through coordinate-sensitive attention weighting. Secondly, a feature enhancement (FE) module with multi-level receptive fields is designed, employing pyramid dilated convolutions and adaptive feature fusion strategies to improve the model's resolution of heterogeneous textures. Thirdly, a multi-task collaborative optimization framework integrating boundary mapping, distance mapping, and mask mapping is constructed, reinforcing spatial cognition of morphologically complex parcels via a joint learning strategy combining geometric constraints and semantic guidance. To validate the model's generalizability, experiments were conducted on multisource remote sensing datasets (Gaofen-2 and Sentinel-2 imagery) covering Shandong and Sichuan regions in China and the Netherlands. Results demonstrate that ESDNet achieves superior performance, surpassing state-of-the-art models by 0.77 percentage points, 2.17 percentage points, and 2.28 percentage points in intersection over union (IoU) across the three regions, respectively. The model's strong generalization capability and high-precision segmentation characteristics provide reliable technical support for dynamic monitoring of cultivated land resources in smart agriculture. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
面向农业资源监测的遥感影像农业地块精准提取是实现耕地资源智能化管理的关键技术。 针对现有深度学习方法在复杂农田场景中面临的边界模糊、纹理多样及形态异构导致的分割精度不足问 题,提出边缘与语义协同优化的多任务神经网络ESDNet,通过3种关键策略实现性能提升。首先,在编 码器与主解码器间嵌入坐标注意力(CA)模块,通过坐标敏感的注意力权重增强模糊边界的鉴别能力;其 次,设计具有多级感受野的特征增强(FE)模块,采用金字塔空洞卷积与自适应特征融合策略提升网络对 异质纹理的解析度;最后,构建边界映射、距离映射与掩膜映射的多任务协同优化框架,通过几何约束与语 义引导的联合学习策略,强化对复杂形态地块的空间认知。为验证网络普适性,实验选取中国山东、四川 及荷兰地区的高分二号、哨兵二号多源遥感影像构建测试集。结果表明,ESDNet在交并比IoU 指标上分 别提升0.77个百分点、2.17个百分点和2.28个百分点,优于现有最优网络,其展现出的强泛化能力和高 精度分割特性,为智慧农业中的耕地资源动态监测提供了可靠的技术支撑. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
