Stella X. Yu : Papers / Google Scholar

Debiased Learning from Naturally Imbalanced Remote Sensing Data
Chun-Hsiao Yeh and Xudong Wang and Stella X. Yu and Charles Hill and Zackery Stech and Scott Kangas
IEEE Conference on Computer Vision and Pattern Recognition Workshop on Multimodal Learning for Earth and Environment, Vancouver, British Columbia, Canada, 19 June 2023
Paper | Slides


Deep learning has shown remarkable success in analyzing grounded imagery, such as consumer photos due to large-scale human annotations are available for dataset, e.g., ImageNet. However, such extensive supervision is not the case for remote sensing data.

We propose a highly effective semi-supervised approach tailored specifically for remote sensing data. Our approach encompasses two key contributions. We adapt the framework from semi-supervised learning approach, such as FixMatch, to remote sensing data by designing a set of robust strong and weak augmentations suitable for this domain. By learning from actual labeled data, combining with pseudo-labeled data, yet address the pseudo-labeling imbalance, we leverage a recently proposed debiased learning approach to mitigate the bias in pseudo-labeling.

Validated by extensive experimentation, our simple semi-supervised framework with 30\% annotations delivers significant accuracy gains over the supervised learning baseline by 7.1\%, and over recent supervised state-of-the-art, CDS by 2.1\% on remote sensing Xview data.

debiased learning, semi-supervised learning, remote-sensing