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

arXiv: https://arxiv.org/abs/2202.04890 · HTML: https://mlsanity.com/paper/2202.04890/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 10 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Discriminative Active Learning](https://mlsanity.com/paper/1907.06347/) (2019)
- [Reinforced active learning for image segmentation](https://mlsanity.com/paper/2002.06583/) (2020)
- [Active learning for object detection in high-resolution satellite images](https://mlsanity.com/paper/2101.02480/) (2021)
- [Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images](https://mlsanity.com/paper/2005.13215/) (2020)

## Built on by (most cited first, 1 of 1)

- [Modulation and signal class labelling using active learning and classification using machine learning](https://mlsanity.com/paper/2202.12930/) (2022)

## Abstract (arXiv)

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.