Remote sensing classification using quantum image processing

Hrithik Kumar; Teymoor Ali; Chris J. Holder; A. Stephen McGough; Deepayan Bhowmik

doi:10.1117/12.3034036

19 November 2024 Remote sensing classification using quantum image processing

Hrithik Kumar, Teymoor Ali, Chris J. Holder, A. Stephen McGough, Deepayan Bhowmik

Proceedings Volume 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX; 131960O (2024) https://doi.org/10.1117/12.3034036
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

Acceleration of image segmentation using deep learning methods on satellite imagery has become ubiquitous in various applications areas such as land cover classification, disaster monitoring and vegetation detection. However, the increase in satellite image resolution and large data volume required for remote sensing applications has resulted in a substantial increase in computational resource usage and demand for real-time processing. This paper investigates quantum computing as a novel approach to meet these computational demands, exploiting its parallel processing strengths. We evaluate hybrid quantum models (COQCNN, MQCNN, FQCNN) against classical CNN and U-Net architectures in remote sensing classification. Although COQCNN and MQCNN underperformed, FQCNN reached 53.26% accuracy, outperforming the classical CNN by 8%. Despite quicker convergence, quantum models struggle with complex feature segmentation, a task where U-Net excels. This study highlights quantum convolutions as a potential path to enhance convergence while addressing challenges like noise from multiple quantum channels affecting accuracy.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hrithik Kumar, Teymoor Ali, Chris J. Holder, A. Stephen McGough, and Deepayan Bhowmik "Remote sensing classification using quantum image processing", Proc. SPIE 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX, 131960O (19 November 2024); https://doi.org/10.1117/12.3034036

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