Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning

TL;DR

This paper introduces a hybrid active learning approach combining diversity and uncertainty techniques to improve aircraft detection in satellite imagery, reducing labeling effort while maintaining high detection performance.

Contribution

The paper presents a novel hybrid clustering active learning method that enhances aircraft detection accuracy with less labeled data in satellite imagery.

Findings

Outperforms other active learning methods in aircraft detection tasks

Reduces labeling effort while maintaining high detection accuracy

Effective for segmentation-based aircraft detection

Abstract

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.