Volume 43 Issue 12
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ZHANG Liangpei, LIU Rong, DU Bo. Hyperspectral Remote Sensing Image Processing by Using Quantum Optimization Algorithm[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1811-1818. DOI: 10.13203/j.whugis20180231
Citation: ZHANG Liangpei, LIU Rong, DU Bo. Hyperspectral Remote Sensing Image Processing by Using Quantum Optimization Algorithm[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1811-1818. DOI: 10.13203/j.whugis20180231
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Hyperspectral Remote Sensing Image Processing by Using Quantum Optimization Algorithm

  • 1.

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • 2.

    School of Computer Science, Wuhan University, Wuhan 430072, China

Funds: 

The National Natural Science Foundation of China 41431175

More Information
  • Author Bio:

    ZHANG Liangpei, PhD, professor, specializes in hyperspectral remote sensing, high resolution remote sensing and remote sensing application. E-mail: zlp62@whu.edu.cn

  • Corresponding author:

    DU Bo, PhD, professor. E-mail: gunspace@163.com

  • Received Date: August 13, 2018
  • Published Date: December 04, 2018
    Graphical Abstract
    • Abstract
  • Hyperspectral remote sensing technology has become an important part of ground observation since the 1980s, and it is the main data source of information acquisition for ground objects. Hyperspectral image (HSIs) not only contains spatial information, but also contains abundant spectral information with tens to hundreds of contiguous spectral bands. The abundant spectral information of HSIs can help us better identify ground objects, which has greatly improved our ability to qualitatively and quantitatively sense the earth's surface. It has been intensively researched to make full use of both spatial and spectral information of HSIs, so as to accurately obtain the information of ground objects. This paper reviews quantum optimization algorithm-based hyperspectral image processing me-thods. The development and methodology of quantum optimization algorithm as well as its application in hyperspectral image processing are introduced. And some suggestion and expectation for further study of the quantum optimization algorithm-based hyperspectral image processing are given.
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    • References (36)
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