  Multi-modal aerial view object classification (MAVOC) in Automatic target
recognition (ATR), although an important and challenging problem, has been
under studied. This paper firstly finds that fine-grained data, class imbalance
and various shooting conditions preclude the representational ability of
general image classification. Moreover, the MAVOC dataset has scene aggregation
characteristics. By exploiting these properties, we propose Scene Clustering
Based Pseudo-labeling Strategy (SCP-Label), a simple yet effective method to
employ in post-processing. The SCP-Label brings greater accuracy by assigning
the same label to objects within the same scene while also mitigating bias and
confusion with model ensembles. Its performance surpasses the official baseline
by a large margin of +20.57% Accuracy on Track 1 (SAR), and +31.86% Accuracy on
Track 2 (SAR+EO), demonstrating the potential of SCP-Label as post-processing.
Finally, we win the championship both on Track1 and Track2 in the CVPR 2022
Perception Beyond the Visible Spectrum (PBVS) Workshop MAVOC Challenge. Our
code is available at https://github.com/HowieChangchn/SCP-Label.
