Optical flow-based moving vehicle detection from single-pass worldview-3 imagery

Satellite-based moving vehicle (MV) detection plays a crucial role in traffic monitoring and surveillance. However, it presents challenges due to the large scale of scenes and the small size of MV features in satellite images. This study presents an optical flow-based approach for MV detection using single-pass WorldView-3 (WV- 3) images. Representative images from two different multispectral sensors in WV-3 were created, and the Gunnar Farneback optical flow algorithm was employed to estimate MV motions. The estimated motion fields were then transformed into the HSV color space and segmented into eight ranges representing different moving direction angles. To refine the MV shapes and eliminate noise, opening operations and the DBSCAN clustering algorithm were applied. Finally, the MV detection results were obtained by combining eight images with bounding boxes. Although the algorithm exhibits high precision, challenges remain in accurately extracting MVs from the motion field, handling adjacent MVs and avoiding misinterpretations of non-moving edge features.