Volume 10 Issue 1
Feb.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(1): 143-158
HUANG Pingping, DUAN Yinghong, TAN Weixian, et al. Change detection method based on fusion difference map in flood disaster[J]. Journal of Radars, 2021, 10(1): 143–158. doi: 10.12000/JR20118
Citation: HUANG Pingping, DUAN Yinghong, TAN Weixian, et al. Change detection method based on fusion difference map in flood disaster[J]. Journal of Radars, 2021, 10(1): 143–158. doi: 10.12000/JR20118
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Change Detection Method Based on Fusion Difference Map in Flood Disaster

doi: 10.12000/JR20118

  • College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China

  • Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China

Funds:  The National Natural Science Foundation of China (61631011), Major Science and Technology Project of Inner Mongolia (2019ZD022), Planned Project of Science and Technology of Inner Mongolia (2019GG139), Innovation Guidance Project of Inner Mongolia (KCBJ2017, KCBJ2018014)
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  • Corresponding author: HUANG Pingping, hpp@imut.edu.cn; TAN Weixian, wxtan@imut.edu.cn
  • Received Date: 2020-08-25
  • Rev Recd Date: 2020-10-27
    • Available Online: 2020-11-10
  • Publish Date: 2021-02-25
    Abstract
  • Due to the influence of the environment on the scattering characteristics of ground objects in flooded areas, the false error rate of the detection results increases when performing change detection on Synthetic Aperture Radar (SAR) images of these areas, which reduces the accuracy of the results obtained for the difference map. To solve this problem, in this paper, we propose a change-detection method based on a fusion difference map. This method combines the regional sensitivity of the entropy difference map with the regional retention of the mean difference map to construct a fusion difference map based on an improved relative entropy and mean value ratio. First, the initial clustering results of the fuzzy local information C-means clustering method are classified by their Pearson correlation coefficients, and second, the secondary classification results are used for the initial image segmentation. Third, the final segmentation results are obtained using the iterative condition model and Markov random field. To verify the flood-disaster-detection performance of the proposed method, we used the second of Europe Remote-Sensing (ERS-2) Satellite data obtained for the Bern area in Switzerland in April and May 1999 and Radarsat remote-sensing data for the Ottawa region in Canada in May and August 1997. We also applied the proposed method to data obtained for the Poyang Lake region of China in June and July 2020, and estimated the disaster area and change trend before and after the flood in Poyang Lake. The experimental results show that the algorithm had a low overall detection error, the false error rate of the detection results were somewhat reduced, and the accuracy of the detection results was improved.

     

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