Academic Journal
Land Cover Classification From Sentinel-2 Images With Quantum-Classical Convolutional Neural Networks
| Title: | Land Cover Classification From Sentinel-2 Images With Quantum-Classical Convolutional Neural Networks |
|---|---|
| Authors: | Fan Fan, Yilei Shi, Xiao Xiang Zhu |
| Source: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 12477-12489 (2024) |
| Publisher Information: | IEEE, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| Subject Terms: | Earth observation (EO), land cover classification, multispectral imagery, quantum circuit, quantum machine learning (QML), remote sensing, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| More Details: | Exploiting machine learning techniques to automatically classify multispectral remote sensing imagery plays a significant role in deriving changes on the Earth’s surface. However, the computation power required to manage large Earth observation data and apply sophisticated machine learning models for this analysis purpose has become an intractable bottleneck. Leveraging quantum computing provides a possibility to tackle this challenge in the future. This article focuses on land cover classification by analyzing Sentinel-2 images with quantum computing. Two hybrid quantum-classical deep learning frameworks are proposed. Both models exploit quantum computing to extract features efficiently from multispectral images and classical computing for final classification. As proof of concept, numerical simulation results on the LCZ42 dataset through the TensorFlow Quantum platform verify our models' validity. The experiments indicate that our models can extract features more effectively compared with their classical counterparts, specifically, the convolutional neural network (CNN) model. Our models demonstrated improvements, with an average test accuracy increase of 4.5% and 3.3%, respectively, in comparison to the CNN model. In addition, our proposed models exhibit better transferability and robustness than CNN models. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1939-1404 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10602728/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3411670 |
| Access URL: | https://doaj.org/article/6d1c83669ff54ad4a593a48073f9747a |
| Accession Number: | edsdoj.6d1c83669ff54ad4a593a48073f9747a |
| Database: | Directory of Open Access Journals |
| ISSN: | 19391404 21511535 |
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| DOI: | 10.1109/JSTARS.2024.3411670 |
| Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Language: | English |