Debiased Learning for Remote Sensing Data
Chun-Hsiao Yeh Xudong Wang Stella X. Yu Charles Hill Zackery Steck Scott Kangas Aaron Reite
Deep learning has had remarkable success at analyzing handheld imagery such
as consumer photos due to the availability of large-scale human annotations
(e.g., ImageNet). However, remote sensing data lacks such extensive annotation
and thus potential for supervised learning. To address this, we propose a
highly effective semi-supervised approach tailored specifically to remote
sensing data. Our approach encompasses two key contributions. First, we adapt
the FixMatch framework to remote sensing data by designing robust strong and
weak augmentations suitable for this domain. Second, we develop an effective
semi-supervised learning method by removing bias in imbalanced training data
resulting from both actual labels and pseudo-labels predicted by the model. Our
simple semi-supervised framework was validated by extensive experimentation.
Using 30\% of labeled annotations, it delivers a 7.1\% accuracy gain over the
supervised learning baseline and a 2.1\% gain over the supervised
state-of-the-art CDS method on the remote sensing xView dataset.