Quantum deep learning in remote sensing: achievements and challenges
Mehdi Khoshboresh-Masouleh, Reza Shah–Hosseini
In recent years, deep learning algorithms have shown promising results for different image analyzing tasks, particularly in remote sensing image processing. Inspired by the success of remote sensing sensors in geo-located imagery, many studies have been carried out on remote sensing sensors for image processing, which brings a new approach into intelligent remote sensing and photogrammetric computer vision. At the same time, algorithms for quantum processors have been shown to efficiently solve some issues that are intractable on conventional, classical processors. This study summarizes the novel techniques of deep learning and quantum deep learning and its research progress and real-world applications in remote sensing image processing, introduces the current main challenges in processing and its development of geo-located datasets, focuses on the analysis and elaboration of the research status of quantum deep learning in sensing and imaging, and on this basis, summarizes the intelligent remote sensing applications and their application effects in scene understanding.
Photonics for Quantum (PfQ)•Jul 9, 2021•2 citations