Remote sensing involves scanning of the earth by satellite or high-flying aircraft and analyzing it. Since the amount of data acquired this way is huge and growing, matchine learning can be used to perform tasks efficently. We are using modern computer vision methods and apply them to different problems in remote sensing.

This research focuses on the development of unsupervised visual anomaly detection methods. Trained on anomaly-free samples only, these methods attempt to remove the need for a difficult acquisition of a diverse set of anomalous objects while aiming to match the performance of supervised methods.

The developed methods allow specialization for large defect detection on various indistrual items such as cracks, smudges, imperfections etc. The methods are learning-based and are thus robust, run realtime and are applicable to a wide range of real problems.