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In some scenarios, a single input image may not be enough to allow the objectclassification. In those cases, it is crucial to explore the complementaryinformation extracted from images presenting the same object from multipleperspectives (or views) in order to enhance the general scene understandingand, consequently, increase the performance. However, this task, commonlycalled multi-view image classification, has a major challenge: missing data. Inthis paper, we propose a novel technique for multi-view image classificationrobust to this problem. The proposed method, based on state-of-the-art deeplearning-based approaches and metric learning, can be easily adapted andexploited in other applications and domains. A systematic evaluation of theproposed algorithm was conducted using two multi-view aerial-ground datasetswith very distinct properties. Results show that the proposed algorithmprovides improvements in multi-view image classification accuracy when comparedto state-of-the-art methods. Code available at\url{https://github.com/Gabriellm2003/remote_sensing_missing_data}.

Facing the Void: Overcoming Missing Data in Multi-View Imagery