Volume 43 Issue 6
Jun.  2018
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WANG Chunyan, LIU Jiaxin, XU Aigong, WANG Yu, SUI Xin. A New Method of Fuzzy Supervised Classification of High Resolution Remote Sensing Image[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 922-929. DOI: 10.13203/j.whugis20150726
Citation: WANG Chunyan, LIU Jiaxin, XU Aigong, WANG Yu, SUI Xin. A New Method of Fuzzy Supervised Classification of High Resolution Remote Sensing Image[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 922-929. DOI: 10.13203/j.whugis20150726
shu

A New Method of Fuzzy Supervised Classification of High Resolution Remote Sensing Image

  • 1.

    School of Mining Industry and Technology, Liaoning Technical University, Huludao 125105, China

  • 2.

    Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China

  • 3.

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Funds: 

The General Science Research Project of Education Bureau of Liaoning Province LJYL036

The General Science Research Project of Education Bureau of Liaoning Province LJYL012

the National Natural Science Foundation of China 41271435

Liaoning College Students' Innovation and Entrepreneurship Training Program Project 201710147000187

More Information
  • Author Bio:

    WANG Chunyan, PhD, specializes in the remotely sensed images modelling and analysis. E-mail: wcy0211@126.com

  • Corresponding author:

    XU Aigong, PhD, professor. E-mail: xu_ag@126.com

  • Received Date: May 05, 2016
  • Published Date: June 04, 2018
    Graphical Abstract
    • Abstract
  • This paper presents a supervised image classification method based on fuzzy membership function to solve incorrect classification of high resolution remote sensing image, which caused by highlight detail information, the uncertainly of the pixels classification derived from the increase of the differences between pixels in the homogenous region, the uncertainly of classification decision and so on. First, Gaussian model is used to characterize the uncertainly of the membership of pixels; then the model is extended to build the image fuzzy membership function to define the uncertainly of the homogenous regions. To segment the image, the objective function is built by linear function of neural network, which the fuzzy membership functions and the membership degrees of the original fuzzy membership functions as input values. The proposed method is compared with the classification methods tested on the WorldView-2 panchromatic synthetic and real images. Through the qualitative and quantitative experiments, it can be found that the proposed method has better classification accuracy.
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    • References (20)
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