International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 667–670, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-667-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 667–670, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-667-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION BASED ON QUANTUM ENTANGLEMENT

F. Yang1,2,3, G. Q. Zhou1,2, J. R. Xiao1,3, Q. Li1,2,3, B. Jia1,2, H. Y. Wang1,2, and J. Gao1,2 F. Yang et al.
  • 1Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian’gan Road, Guilin, Guangxi 541004, China
  • 2Department of Mechanical and Control Engineering, Guilin University of Technology, No. 12 Jian’gan Road, Guilin, Guangxi 541004, China
  • 3College of Science, Guilin University of Technology, Guilin 541004, China

Keywords: Image classification, Quantum entanglement, Multispectral remote sensing

Abstract. 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.