融合雷达与光学数据，提升森林高度估算精度
Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data
- 用雷达相干性结合光学影像扩展特征空间
- 相比原模型，RMSE降13.5%，MAE降16.6%
- 适合需要高精度森林参数的遥感研究者
将机器学习（ML）与物理模型（PM）结合，已成为从遥感数据中反演地物参数的有力方法。本文提出一种基于TanDEM-X干涉相干性的森林高度估算混合模型，通过物理模型约束学习过程。尽管所选特征保证了物理解释性，仍无法完全消除高度/结构与基线/地形坡度之间的模糊性。为此，引入光学Landsat数据扩展特征空间，提供林型或结构的互补信息。模型在加蓬洛佩国家公园多个TanDEM-X数据集上验证，并与机载LiDAR测量对比。结果表明，相比原始混合模型，均方根误差（RMSE）降低13.5%，平均绝对误差（MAE）降低16.6%，证实多光谱输入的增益价值。
原文摘要 · Abstract (English)
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.
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