Abstract: The application of remote sensing is a commonly used approach to environmental monitoring in mine areas. The research on convolutional neural network (CNN) for recognition of opencast mining area in high-resolution remote sensing image could help to improve monitoring efficiency. This paper focused on the problem of low classification accuracy of opencast mining area based on CNN due to insufficient trained datasets, the experiment is designed with three types of transfer learning methods and tested in different pre-trained CNN models. The analysis shows that by contrast, fixed lower layers’ parameters in pre-trained CNN models and fine-tune higher layers’ parameters is the optimal training method, it achieved over 87% in both producer’s accuracy and user’s accuracy. This experimental results indicate that opencast mining area could be effectively recognized in high-resolution imagery based on this training method, therefore, the CNN which trained by this method can be used as an aid in remote sensing interpretation of opencast mining area.