做遥感或森林生态研究的团队，这个模型直接帮你把森林高度估算误差砍掉一成多，而且方法可复现，建议点开看具体实现。
该研究提出一种混合机器学习模型，将物理模型约束与光学Landsat数据结合，用于从TanDEM-X干涉相干数据估算森林高度。通过引入Landsat多光谱信息，模型能更好区分森林类型和结构，解决原始数据中的高度/结构与基线/地形坡度歧义。在加蓬Lopé国家公园的验证中，相比原混合模型，RMSE降低13.5%，MAE降低16.6%。这项工作展示了多源遥感数据融合对提升地球物理参数反演精度的价值。
Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data
Integrating machine learning (ML) with physical models (PM) has emerged as a promising way of retrieving geophysical parameters from remote sensing data. In this context, a ML model for estimating forest height from TanDEM-X interferometric coherence measurements has recently been proposed, that constrains the learning process through a PM. While the features used for training and inversion where selected to ensure the physical consistency of the solutions, they could not resolve all height / structure and baseline / terrain slope ambiguities in the data. To improve this, the extension of the feature space with optical Landsat data is proposed able to provide complementary information on forest type or structure. The extended model is applied and validated on several TanDEM-X acquisitions over the Gabonese Lopé national park site and assessed against airborne LiDAR measurements. Results show a 13.5% reduction in RMSE and a 16.6% reduction in MAE compared to the original hybrid model, confirming the added value of multispectral inputs.