Multi-modal Aerial View Image Challenge: SAR Classification

Spencer Low, Oliver Nina, Dylan Bowald, Angel D. Sappa, Nathan Inkawhich, Peter Bruns; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3105-3112


This manuscript delineates the outcomes of the fourth Multi-modal Aerial View Image Challenge - Classification (MAVIC-C). The challenge is aimed at advancing the development of recognition models that leverage Synthetic Aperture Radar (SAR) and Electro-Optical (EO) imagery. Encouraging the integration of data from these two distinct modalities the challenge seeks to foster the creation of multi-modal approaches that complement characteristics of SAR and EO information. Building upon the precedents set in previous years the 2021 MAVOC challenge validated the potential of integrating SAR and EO modalities. The subsequent 2022 and 2023 challenges further explored the capabilities of multi-modal frameworks. In its latest iteration the 2024 challenge presents an enhanced UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset alongside a revised competition format focused on the task of SAR classification. The 2024 challenge evaluates model robustness through out-of-distribution measures alongside traditional accuracy metrics. The core of this paper is devoted to analyzing the methodologies of the top-performing entries and their performance metrics on a blind test set.

Related Material

@InProceedings{Low_2024_CVPR, author = {Low, Spencer and Nina, Oliver and Bowald, Dylan and Sappa, Angel D. and Inkawhich, Nathan and Bruns, Peter}, title = {Multi-modal Aerial View Image Challenge: SAR Classification}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {3105-3112} }