Multi-Modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023

Spencer Low, Oliver Nina, Angel Domingo Sappa, Erik Blasch, Nathan Inkawhich

CVPRW 2023 pp. 515-523

doi:10.1109/CVPRW59228.2023.00058 /cvprw/2023/low2023cvprw-multimodal-a/

Abstract

This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations.

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Cite

Text

Low et al. "Multi-Modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00058

Markdown

[Low et al. "Multi-Modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/low2023cvprw-multimodal-a/) doi:10.1109/CVPRW59228.2023.00058

BibTeX

@inproceedings{low2023cvprw-multimodal-a,
  title     = {{Multi-Modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023}},
  author    = {Low, Spencer and Nina, Oliver and Sappa, Angel Domingo and Blasch, Erik and Inkawhich, Nathan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {515-523},
  doi       = {10.1109/CVPRW59228.2023.00058},
  url       = {https://mlanthology.org/cvprw/2023/low2023cvprw-multimodal-a/}
}