{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T20:06:11Z","timestamp":1779221171703,"version":"3.51.4"},"reference-count":60,"publisher":"National Library of Serbia","issue":"2","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:p>Remote sensing image classification is a fundamental task for Earth observation and environmental monitoring. However, conventional convolutional neural networks (CNNs) are limited by computational capacity and struggle to efficiently process the rapidly growing volume of remote sensing data. To address this limitation, we propose HAQCCN (Hybrid Asymmetric Quantum\u2013Classical Convolutional Network), a novel hybrid architecture that integrates quantum computation into the classical convolutional framework through asymmetric quantum convolutional circuits. In HAQCCN, the asymmetric quantum circuits enable a limited number of qubits to process more classical data while maintaining excellent feature extraction capability. Experiments conducted on the IBM Qiskit platform using the Overhead-MNIST, PatternNet, and RSI-CB256 datasets demonstrate that HAQCCN outperforms conventional CNNs and existing quantum models. Furthermore, we systematically investigate the effects of encoding schemes, the number of quantum convolutional kernels, and the number of qubits on model performance, confirming the effectiveness and scalability of the proposed method for remote sensing image classification.<\/jats:p>","DOI":"10.2298\/csis251029015c","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T10:09:26Z","timestamp":1769681366000},"page":"861-884","source":"Crossref","is-referenced-by-count":0,"title":["HAQCCN: A Hybrid Quantum\u2013Classical Convolutional Network with asymmetric kernels for remote sensing image classification"],"prefix":"10.2298","volume":"23","author":[{"given":"Lianghai","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology Xiangtan, China + Sanya Institute of Hunan University of Science and Technology Sanya, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology Xiangtan, China + Sanya Institute of Hunan University of Science and Technology Sanya, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology Xiangtan, China + Sanya Institute of Hunan University of Science and Technology Sanya, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoliang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology Xiangtan, China + Sanya Institute of Hunan University of Science and Technology Sanya, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaning","family":"Song","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Manufacturing, Hechi University Hechi, Guangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Anderson, K., Ryan, B., Sonntag, W., Kavvada, A., Friedl, L.: Earth observation in service of the 2030 agenda for sustainable development. 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