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Multi-modal aerial view object classification (MAVOC) in Automatic targetrecognition (ATR), although an important and challenging problem, has beenunder studied. This paper firstly finds that fine-grained data, class imbalanceand various shooting conditions preclude the representational ability ofgeneral image classification. Moreover, the MAVOC dataset has scene aggregationcharacteristics. By exploiting these properties, we propose Scene ClusteringBased Pseudo-labeling Strategy (SCP-Label), a simple yet effective method toemploy in post-processing. The SCP-Label brings greater accuracy by assigningthe same label to objects within the same scene while also mitigating bias andconfusion with model ensembles. Its performance surpasses the official baselineby a large margin of +20.57% Accuracy on Track 1 (SAR), and +31.86% Accuracy onTrack 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 2022Perception Beyond the Visible Spectrum (PBVS) Workshop MAVOC Challenge. Ourcode is available at https://github.com/HowieChangchn/SCP-Label.

Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification