Empirical Analysis of Keypoint-based Techniques for Onboard Band-to-Band Alignment

Automated Band-to-Band alignment is a crucial prerequisite for satellite mission involving image operations. Many techniques are now developed for image registration: correlation-based methods, feature-based methods, hybrid methods and Deep Learning-based methods. Due to perturbations in the spacecraft orientation and dynamic disturbances, the a priori knowledge concerning the misalignment of bands is often insufficient, therefore an appropriate sub-pixel level registration scheme must be implemented on-board the satellite. In this work three registration approaches are tested and compared using raw image data from the European Space Agency Sentinel-2 mission: the Coarse Coregistration, SuperGlue Coregistration and LightGlue Coregistration. The Coarse Coregistration is a simple approach to image registration based on the deterministic shift of the sensed image, but its performance is affected by non-systematic disturbances that are not corrected, often limiting it is accuracy to a pixel level. On the other hand, SuperGlue is a powerful but computationally expensive deep network designed for computer vision tasks. Despite its excellent accuracy, the inference time does not suit the on-board requirements of the mission. For this reason, a lighter version of SuperGlue based on an adaptive network, named LightGlue, has been tested to study its performance and inference times. The maximum number of keypoints, the adaptive depth and the number of layers of the network have been modified in order to test the architecture's response. With this analysis, it has been found that LightGlue brings the precision on the correcting shift up to a sub-pixel level, up to an order of magnitude better than the Coarse Coregistration, while running up to 1.5 times faster than SuperGlue. Sentinel-2 raw data is used here for the first time.