Journal of Geodesy and Geoinformation Science ›› 2022, Vol. 5 ›› Issue (1): 91-102.doi: 10.11947/j.JGGS.2022.0109

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A Novel Unsupervised Change Detection Method with Structure Consistency and GFLICM Based on UAV Images

Wensong LIU1(),Xinyuan JI1,Jie LIU2,Fengcheng GUO1(),Zongqiao YU1   

  1. 1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
    2. Geological Affairs Center of Tianjin, Tianjin 300040, China
  • Received:2021-09-18 Accepted:2022-01-03 Online:2022-03-20 Published:2022-03-31
  • Contact: Fengcheng GUO;
  • About author:Wensong LIU(1988-), male, PhD, majors in images processing and change detection. E-mail:
  • Supported by:
    National Natural Science Foundation of China(62101219);Natural Science Foundation of Jiangsu Province(BK20201026);Natural Science Foundation of Jiangsu Province(BK20210921);Science Foundation of Jiangsu Normal University(19XSRX006);Open Research Fund of Jiangsu Key Laboratory of Resources and Environmental Information Engineering(JS202107)


With the rapid development of Unmanned Aerial Vehicle (UAV) technology, change detection methods based on UAV images have been extensively studied. However, the imaging of UAV sensors is susceptible to environmental interference, which leads to great differences of same object between UAV images. Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection. To address this issue, a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model (GFLICM) was proposed in this study. Within this method, the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images. The local variation coefficient was introduced and a new fuzzy factor was reconstructed, after which the GFLICM algorithm was used to analyze difference images. Finally, change detection results were analyzed qualitatively and quantitatively. To measure the feasibility and robustness of the proposed method, experiments were conducted using two data sets from the cities of Yangzhou and Nanjing. The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.

Key words: change detection; UAV images; graph model; structural consistency; Generalized Fuzzy Local Information C-means Clustering Model (GFLICM)