Building height is an important indicator for scientific research and
practical application. However, building height products with a high spatial
resolution (10m) are still very scarce. To meet the needs of high-resolution
building height estimation models, this study established a set of
spatial-spectral-temporal feature databases, combining SAR data provided by
Sentinel-1, optical data provided by Sentinel-2, and shape data provided by
building footprints. The statistical indicators on the time scale are extracted
to form a rich database of 160 features. This study combined with permutation
feature importance, Shapley Additive Explanations, and Random Forest variable
importance, and the final stable features are obtained through an expert
scoring system. This study took 12 large, medium, and small cities in the
United States as the training data. It used moving windows to aggregate the
pixels to solve the impact of SAR image displacement and building shadows. This
study built a building height model based on a random forest model and compared
three model ensemble methods of bagging, boosting, and stacking. To evaluate
the accuracy of the prediction results, this study collected Lidar data in the
test area, and the evaluation results showed that its R-Square reached 0.78,
which can prove that the building height can be obtained effectively. The fast
production of high-resolution building height data can support large-scale
scientific research and application in many fields.

通过结合 Sentinel-1 的 SAR 数据、Sentinel-2 的光学数据和建筑物轮廓提供的形状数据，本研究建立了一套空间 - 光谱 - 时间特征数据库，提取这些特征并建立了基于随机森林模型的建筑物高度模型，通过模型集成方法和 Lidar 数据的验证，实现了高分辨率建筑物高度数据的快速生成。