ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLII-3-W10-667-2020 MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION BASED ON QUANTUM ENTANGLEMENT Yang F. 1 2 3 Zhou G. Q. 1 2 Xiao J. R. 1 3 Li Q. 1 2 3 Jia B. 1 2 Wang H. Y. 1 2 Gao J. 1 2 Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian’gan Road, Guilin, Guangxi 541004, China Department of Mechanical and Control Engineering, Guilin University of Technology, No. 12 Jian’gan Road, Guilin, Guangxi 541004, China College of Science, Guilin University of Technology, Guilin 541004, China 07 02 2020 XLII-3/W10 667 670 Copyright: © 2020 F. Yang et al. 2020 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLII-3-W10/667/2020/isprs-archives-XLII-3-W10-667-2020.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLII-3-W10/667/2020/isprs-archives-XLII-3-W10-667-2020.pdf

Aiming at the problems of low accuracy and slow speed in the current remote sensing image classification algorithm,In order to improve remote sensing image classification, a quantum entanglement algorithm is proposed.The model transforms the classification process of remote sensing image into a random self-organization process of quantum particles in the state configuration space. The state configuration formed by entanglement of quantum particles evolves with time and finally converges to an average probability distribution.Taking Kunming city of Yunnan province as the research area, this paper compares the classification method in this paper with the traditional remote sensing classification method by using the 02C image data of yuanyuan1.Compared with other classification methods, the classification accuracy of this paper meets the requirements.