Towards Bundle Adjustment for Satellite Imaging via Quantum Machine Learning

TL;DR

This paper explores quantum machine learning techniques for satellite image alignment, focusing on keypoint extraction and feature matching, and evaluates their performance on current quantum hardware compared to classical methods.

Contribution

It reformulates clustering and matching algorithms for quantum computers and assesses their potential to outperform classical systems in satellite image processing.

Findings

Classical systems outperform current quantum methods.

Quantum algorithms are ready for future quantum hardware improvements.

Experimental results on quantum emulation hardware demonstrate feasibility.

Abstract

Given is a set of images, where all images show views of the same area at different points in time and from different viewpoints. The task is the alignment 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 on quantum methods for keypoint extraction and feature matching, due to the demanding computational complexity of these sub-tasks. To this end, k-medoids clustering, kernel density clustering, nearest neighbor search, and kernel methods are investigated and it is explained how these methods can be re-formulated for quantum annealers and gate-based quantum computers. Experimental results obtained on digital quantum emulation hardware, quantum annealers, and quantum gate computers show that classical systems still deliver superior results. However, the proposed methods are ready for the current and upcoming generations of quantum computing devices which have the potential to outperform classical systems in the near future.