发表机构
School of Computer Science, Sichuan University Jinjiang College（四川大学锦江学院计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TRNet模型针对山区丘陵水稻分割难题，采用双编码器与地形引导解码，在A、B区域水稻IoU较双编码器U-Net显著提升，验证了地形作为上下文先验的有效性。
AI 中文摘要
从山区和丘陵地区的超高分辨率影像中绘制水稻分布十分困难，因为地形会改变光学外观，并增加与视觉相似植被的混淆。本文针对0.5米高分一号（GaoJing-1）红-绿-蓝（RGB）影像、5米TanDEM-X数字高程模型（DEM）及衍生坡度数据，提出了TRNet模型。该模型采用独立的视觉编码器和地形编码器，以保留模态特定特征；在编码器早期阶段，地形能量-光谱校正模块应用受地形条件约束的低频调制与非对称高频调控，以抑制陡坡杂波并针对性增强缓坡水稻特征；地形引导水稻结构解码器则结合语义、水稻-背景边界及内部线索，利用粗分辨率地形作为上下文信息。实验采用内部测试集A区域与独立测试集B区域，B区域地形更陡峭、水稻占比更低。TRNet在A、B区域的水稻交并比（IoU）分别达到85.10%和80.68%，较原始双编码器U-Net分别提升9.15和18.83个百分点。消融实验及坡度分层结果表明，这些性能提升源于频率校正、结构学习以及陡坡误报减少，研究结果支持粗分辨率地形可作为超高分辨率水稻绘制的上下文先验。
英文摘要
Mapping paddy rice from very high resolution (VHR) imagery in mountainous and hilly regions remains challenging because terrain variations alter optical appearance and increase confusion with visually similar vegetation. To address this issue, we propose TRNet for multimodal paddy rice segmentation using 0.5 m GaoJing 1 red green blue (RGB) imagery, a 5 m TanDEM X digital elevation model (DEM), and derived slope information. TRNet employs separate visual and terrain encoders to preserve modality specific representations. At an early encoder stage, the proposed Topographic Energy Spectral Rectification (TESR) performs terrain conditioned low frequency modulation and asymmetric high frequency regulation to suppress steep slope clutter while selectively enhancing rice related cues on compatible low slope regions. The Topography Guided Paddy Structure Decoder (TPSD) further integrates semantic, rice background boundary, and interior cues with coarse topographic context to refine structural predictions. Experiments are conducted on an Area A internal test set and a geographically held out Area B with steeper terrain and lower rice prevalence. TRNet achieves Rice IoU scores of 85.10% and 80.68% on Areas A and B, outperforming the original Dual Encoder U Net by 9.15 and 18.83 percentage points, respectively. Without any adaptation, evaluation on matched August 2024 imagery retains Rice IoU scores of 82.04% and 76.12%. Extensive ablation, slope stratified, and cross year seasonal analyses demonstrate that the improvements arise from effective frequency rectification and structure learning, which reduce steep terrain false positives and low slope rice omissions. These results demonstrate that coarse topography can serve as a stable contextual prior for robust VHR paddy rice mapping.
Comments15 pages, 10 figures, 7 tables
