Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning
This work addresses the need for efficient aircraft detection in defense applications by reducing reliance on expert-labeled data, though it appears incremental as it builds on existing active learning techniques.
The paper tackled the problem of reducing labeling effort for aircraft detection in satellite imagery by proposing a hybrid active learning method that combines diversity- and uncertainty-based selection, achieving better or competitive results compared to other active learning methods.
The earth observation industry provides satellite imagery with high spatial resolution and short revisit time. To allow efficient operational employment of these images, automating certain tasks has become necessary. In the defense domain, aircraft detection on satellite imagery is a valuable tool for analysts. Obtaining high performance detectors on such a task can only be achieved by leveraging deep learning and thus us-ing a large amount of labeled data. To obtain labels of a high enough quality, the knowledge of military experts is needed.We propose a hybrid clustering active learning method to select the most relevant data to label, thus limiting the amount of data required and further improving the performances. It combines diversity- and uncertainty-based active learning selection methods. For aircraft detection by segmentation, we show that this method can provide better or competitive results compared to other active learning methods.