  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.
