Volume 47 Issue 2
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ZHANG Huifang, ZHANG Penglin, CHAO Jian. Change Detection by Multi-scale Fuzzy Fusion on High Resolution Images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(2): 296-303. DOI: 10.13203/j.whugis20190425
Citation: ZHANG Huifang, ZHANG Penglin, CHAO Jian. Change Detection by Multi-scale Fuzzy Fusion on High Resolution Images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(2): 296-303. DOI: 10.13203/j.whugis20190425
shu

Change Detection by Multi-scale Fuzzy Fusion on High Resolution Images

  • 1.

    School of Remote Sensing and Information Engnieering, Wuhan University, Wuhan 430079, China

  • 2.

    Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resource, Shenzhen 518000, China

  • 3.

    Geomatics Technology and Application Key Laboratory of Qinghai Province, Xining 810001, China

Funds: 

The Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resource KF-2019-04-046

More Information
  • Author Bio:

    ZHANG Huifang, master, specializes inreliable change detection and geographic information system. E-mail: rszhf@whu.edu.cn

  • Corresponding author:

    ZHANG Penglin, PhD, professor. E-mail: zpl@whu.edu.cn

  • Received Date: October 11, 2021
  • Published Date: February 04, 2022
    Graphical Abstract
    • Abstract
  •   Objectives  With the development of remote sensing technology, the spatial resolution of remote sensing image keeps improving, which brings both opportunities and challenges for the traditional remote sensing image classification and change detection.In order to improve the reliability of change detection of high resolution remote sensing image, this paper proposes a method of change detection of remote sensing image based on fuzzy comprehensive evaluation.
      Methods  Firstly, images of two-phases were overlapped to a new image which can be segmented at multi-scale. Secondly, a fuzzy comprehensive evaluation model was established for objects in certain scale to calculate membership of pixels in each object, which took the spectral and texture characteristics of two-phase remote sensing image objects into comprehensive consideration. Finally, the entropy method was used to fuse the fuzzy evaluation membership degree of each pixel in different scales based on information entropy.
      Results  Taking two groups of high resolution images with different phases as examples, we realized the effective fusion of multi-scale change detection based on fuzzy logic, which made full use of multi-level pixel features and consequently improved the overall effect of single scale object-oriented change detection.
      Conclusions  The proposed method provides a new idea for the exploration of multi-scale change detection.
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    • References (24)
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