Deep Active Learning for Remote Sensing Object Detection
This work addresses the problem of reducing annotation effort for remote sensing object detection, offering a domain-specific incremental improvement.
The paper tackles the high annotation cost for CNN object detectors in remote sensing by proposing an uncertainty-based active learning method that selects informative images for annotation, achieving same-level performance as full supervision with only half the images and outperforming it with 55% of images using augmented weights.
Recently, CNN object detectors have achieved high accuracy on remote sensing images but require huge labor and time costs on annotation. In this paper, we propose a new uncertainty-based active learning which can select images with more information for annotation and detector can still reach high performance with a fraction of the training images. Our method not only analyzes objects' classification uncertainty to find least confident objects but also considers their regression uncertainty to declare outliers. Besides, we bring out two extra weights to overcome two difficulties in remote sensing datasets, class-imbalance and difference in images' objects amount. We experiment our active learning algorithm on DOTA dataset with CenterNet as object detector. We achieve same-level performance as full supervision with only half images. We even override full supervision with 55% images and augmented weights on least confident images.