The invention discloses a SAR image change detection method based on a multi-granularity cascade forest model, which mainly solves the problem of strong dependence of a traditional method on a difference map. The implementation scheme is that two registered multi-temporal SAR images I1 and I2 are given. A logarithmic ratio operator is used to generate a difference graph Id for I1 and I2; For Id, the Hall-like features are extracted and inputted into the trained multi-granularity cascade forest model, and two probability maps I1 and I0 are generated, which form a new feature with the differencemap Id. The new features is used to retrain the model, New prediction results are obtained. TIF The Kappa coefficient is compared with the Kappa coefficient of the last time, the prediction result with higher coefficient is selected as the final change detection result. The invention can effectively suppress the influence of the difference map on the final result, improve the accuracy of the change detection, and can be used for environmental detection and disaster detection.
SAR image change detection method based on multi-granularity cascade forest model