The invention belongs to the technical field of multi-temporal unmanned aerial vehicle image change detection, and particularly relates to an unmanned aerial vehicle image change detection method based on semantic segmentation and a twin neural network. The method comprises the following steps: S1, expanding a data set and dividing the data set; S2, establishing a deep neural network model based on combination of a semantic segmentation framework DeeplabV3 and a twin network; S3, training a DeeplabV3-based twin neural network model by using the training data set; and S4, based on the test dataset and the trained model, verifying a training result. According to the method, the semantic segmentation thought is combined, the weight sharing characteristic of the twinning network is utilized,features with realistic meanings are extracted, the semantic relation between pixels and the multi-scale problem of a change area are considered, the problems of noise sensitivity, low change detection precision and the like are solved, and the quality and robustness of a difference graph are improved.