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OBJECTIVE: The purpose of this study is to realize the precise detection of Synthetic Aperture Radar (SAR) image changes. METHODS: In this study, an intuitionistic fuzzy C-means clustering algorithm is used to accurately detect the target changes in SAR images. The change of SAR image is detected by the constructed intuitionistic fuzzy C-means clustering algorithm. Then, the effect of intuitionistic fuzzy C-means clustering algorithm, block principal component analysis (PCA) and logarithmic ratio method is compared and analyzed in the aspects of stability, accuracy, image extraction, restoration, error and work efficiency of the algorithm. RESULTS: Compared with block PCA and logarithmic ratio methods, intuitionistic fuzzy C-means clustering algorithm has obvious advantages in stability, with standard deviation of 0.010 and other two algorithms of 0.014 and 0.017. In terms of detection accuracy and error, the algorithm in this study also has a good performance, and the detection accuracy can reach 92.4%. In addition, the intuitionistic fuzzy C-means clustering algorithm is clear and efficient for SAR image target extraction and restoration. Compared with the other two algorithms, the algorithm in this study improves by at least 20% in operation speed. There is no significant difference in the detection results of the proposed algorithm for SAR images with different targets, such as objects, people, geographical environment, etc. CONCLUSION: In this study, based on intuitionistic fuzzy C-means clustering algorithm, target changes in SAR images are detected, and the operation of the algorithm is studied. The algorithm used in this study shows a relatively comprehensive and good result, and also shows that the algorithm is a comprehensive result, which requires a good operation at many levels. This research greatly improves the recognition of intuitionistic fuzzy C-means clustering algorithm and SAR image. [ABSTRACT FROM AUTHOR]