NTIRE 2021 Multi-modal Aerial View Object Classification Challenge
This addresses the problem of aerial object classification for computer vision researchers, but it is incremental as it focuses on a new benchmark challenge.
The paper introduced the first Multi-modal Aerial View Object Classification Challenge (MAVOC) at NTIRE 2021, analyzing how to use EO and SAR imagery complementarily, and reported results showing over 15% accuracy improvement from baselines for each track.
In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO andSAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition