Active learning for object detection in high-resolution satellite images
This work is significant for defense-related remote sensing, where military experts' time is limited, by reducing the data labeling effort for object detection.
This study reviews active learning techniques for object detection in high-resolution satellite images, demonstrating their value for aircraft detection. The research addresses the challenge of extensive labeling efforts required for supervised deep learning in defense-related remote sensing applications.
In machine learning, the term active learning regroups techniques that aim at selecting the most useful data to label from a large pool of unlabelled examples. While supervised deep learning techniques have shown to be increasingly efficient on many applications, they require a huge number of labelled examples to reach operational performances. Therefore, the labelling effort linked to the creation of the datasets required is also increasing. When working on defense-related remote sensing applications, labelling can be challenging due to the large areas covered and often requires military experts who are rare and whose time is primarily dedicated to operational needs. Limiting the labelling effort is thus of utmost importance. This study aims at reviewing the most relevant active learning techniques to be used for object detection on very high resolution imagery and shows an example of the value of such techniques on a relevant operational use case: aircraft detection.