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Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection
In defense-related remote sensing applications, such as vehicle detection on satellite imagery, supervised learning requires a huge number of labeled examples to reach operational performances.
Ensemble Learning techniques for object detection in high-resolution satellite images
Ensembling is a method that aims to maximize the detection performance by fusing individual detectors.
Case-based reasoning for rare events prediction on strategic sites
Satellite imagery is now widely used in the defense sector for monitoring locations of interest.
Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning
In the defense domain, aircraft detection on satellite imagery is a valuable tool for analysts.
Neural Architecture Search in operational context: a remote sensing case-study
Neural Architecture Search (NAS) is a framework introduced to mitigate such risks by jointly optimizing the network architectures and its weights.
Active learning for object detection in high-resolution satellite images
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.
Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images
Detecting and identifying objects in satellite images is a very challenging task: objects of interest are often very small and features can be difficult to recognize even using very high resolution imagery.
