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.