用可见光+红外影像推算森林结构，精度接近激光雷达。
FSKD: Monocular Forest Structure Inference via LiDAR-to-RGBI Knowledge Distillation
- 用多模态教师融合激光雷达与影像数据，指导仅用影像的轻量学生模型。
- 零样本下冠层高程图误差仅4.17米，比现有方法低29%~46%。
- 可同时预测树高、叶面积指数等多指标，适合大规模生态监测。
在个体树木尺度上获取高分辨率森林结构数据对碳汇、生物多样性和生态系统监测至关重要。尽管机载激光雷达是林分结构指标（如冠层高程图CHM、叶面积指数PAI、叶层高度多样性FHD）的参考标准，但其成本高且采集频率低。本文提出FSKD：一种激光雷达到可见光-红外（RGBI）知识蒸馏框架，其中多模态教师通过交叉注意力融合RGBI影像与激光雷达导出的平面度量和垂直剖面，而仅使用RGBI的SegFormer学生模型学习复现这些输出。该框架在德国萨克森州384平方公里森林（20厘米地面采样距离）上训练，并在八个地理分布不同的测试区域进行评估。学生模型实现最先进的零样本CHM性能（中位绝对误差4.17米，R²=0.51，IoU=0.87），MAE比HRCHM/DAC基线低29%~46%（5.81米对比8.14~10.84米），相关系数更强（0.713对比0.166~0.652）。消融实验表明，多模态融合使性能提升10%~26%，且适当容量的非对称蒸馏至关重要。该方法可联合预测CHM、PAI和FHD，这是当前单目CHM估计器不具备的多指标能力，尽管PAI/FHD迁移仍具区域依赖性，需局部校准。框架在时间不匹配场景下（冬季激光雷达，夏季RGBI）依然有效，打破了严格同步采集限制，支持20厘米分辨率的可扩展运行监测，适用于数字孪生德国等国家项目。
原文摘要 · Abstract (English)
Very High Resolution (VHR) forest structure data at individual-tree scale is essential for carbon, biodiversity, and ecosystem monitoring. Still, airborne LiDAR remains costly and infrequent despite being the reference for forest structure metrics like Canopy Height Model (CHM), Plant Area Index (PAI), and Foliage Height Diversity (FHD). We propose FSKD: a LiDAR-to-RGB-Infrared (RGBI) knowledge distillation (KD) framework in which a multi-modal teacher fuses RGBI imagery with LiDAR-derived planar metrics and vertical profiles via cross-attention, and an RGBI-only SegFormer student learns to reproduce these outputs. Trained on 384 $km^2$ of forests in Saxony, Germany (20 cm ground sampling distance (GSD)) and evaluated on eight geographically distinct test tiles, the student achieves state-of-the-art (SOTA) zero-shot CHM performance (MedAE 4.17 m, $R^2$=0.51, IoU 0.87), outperforming HRCHM/DAC baselines by 29--46% in MAE (5.81 m vs. 8.14--10.84 m) with stronger correlation coefficients (0.713 vs. 0.166--0.652). Ablations show that multi-modal fusion improves performance by 10--26% over RGBI-only training, and that asymmetric distillation with appropriate model capacity is critical. The method jointly predicts CHM, PAI, and FHD, a multi-metric capability not provided by current monocular CHM estimators, although PAI/FHD transfer remains region-dependent and benefits from local calibration. The framework also remains effective under temporal mismatch (winter LiDAR, summer RGBI), removing strict co-acquisition constraints and enabling scalable 20 cm operational monitoring for workflows such as Digital Twin Germany and national Digital Orthophoto programs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。