Quantum annealing for remote sensing data processing: A review of optimization based applications

Remote sensing data, generated from satellites, drones, and airborne sensors, has become increasingly complex and voluminous, posing significant challenges in efficient processing and analysis. Traditional optimization techniques often struggle with these high-dimensional, non-linear, and NP-hard problems. Quantum annealing, a quantum computing paradigm, offers a novel approach to solving combinatorial optimization problems by leveraging quantum mechanical principles such as quantum superposition and tunneling. This review addresses the growing need for advanced computational techniques in remote sensing data processing, specifically focusing on how quantum annealing can enhance tasks such as image classification, feature selection, clustering, and inverse modelling. Through the formulation of optimization tasks as Quadratic Unconstrained Binary Optimization (QUBO) problems, we explore the potential of quantum annealing in improving both accuracy and computational efficiency when compared to classical methods. Additionally, the paper highlights current limitations in quantum hardware and discusses emerging hybrid quantum-classical strategies to overcome these challenges. By offering a comprehensive review, we aim to pave the way for future advancements in quantum-enhanced geospatial intelligence and remote sensing analytics.