  Ensemble methods are a reliable way to combine several models to achieve
superior performance. However, research on the application of ensemble methods
in the remote sensing object detection scenario is mostly overlooked. Two
problems arise. First, one unique characteristic of remote sensing object
detection is the Oriented Bounding Boxes (OBB) of the objects and the fusion of
multiple OBBs requires further research attention. Second, the widely used deep
learning object detectors provide a score for each detected object as an
indicator of confidence, but how to use these indicators effectively in an
ensemble method remains a problem. Trying to address these problems, this paper
proposes OBBStacking, an ensemble method that is compatible with OBBs and
combines the detection results in a learned fashion. This ensemble method helps
take 1st place in the Challenge Track \textit{Fine-grained Object Recognition
in High-Resolution Optical Images}, which was featured in \textit{2021 Gaofen
Challenge on Automated High-Resolution Earth Observation Image Interpretation}.
The experiments on DOTA dataset and FAIR1M dataset demonstrate the improved
performance of OBBStacking and the features of OBBStacking are analyzed.
