On Learning Vehicle Detection in Satellite Video
This work addresses vehicle detection for remote sensing applications, but it is incremental as it adapts existing deep learning methods to a new modality.
The paper tackles vehicle detection in satellite video, a challenging task due to small object sizes, by applying deep learning methods from wide-area motion imagery to achieve an F1 score of 0.84 on Planet's SkySat-1 LasVegas video, showing comparable results with potential for improvement.
Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moderate performance (0.26 - 0.82 $F_1$ score). This work proposes to apply recent work on deep learning for wide-area motion imagery (WAMI) on satellite video. We show in a first approach comparable results (0.84 $F_1$) on Planet's SkySat-1 LasVegas video with room for further improvement.