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Given is a set of images, where all images show views of the same area atdifferent points in time and from different viewpoints. The task is thealignment of all images such that relevant information, e.g., poses, changes,and terrain, can be extracted from the fused image. In this work, we focus onquantum methods for keypoint extraction and feature matching, due to thedemanding computational complexity of these sub-tasks. To this end, k-medoidsclustering, kernel density clustering, nearest neighbor search, and kernelmethods are investigated and it is explained how these methods can bere-formulated for quantum annealers and gate-based quantum computers.Experimental results obtained on digital quantum emulation hardware, quantumannealers, and quantum gate computers show that classical systems still deliversuperior results. However, the proposed methods are ready for the current andupcoming generations of quantum computing devices which have the potential tooutperform classical systems in the near future.

Towards Bundle Adjustment for Satellite Imaging via Quantum Machine Learning