Fig. 1. Network structure of DeepLabv3+ basic model

Fig. 2. Model structure of improved DeepLabv3+

Fig. 3. Hollow convolution of fusion of different receptive fields. (a) Channel stitching; (b) sampling point distribution of r=12 convolutional layer in original feature map; (c) sampling point distribution of r=12 convolutional layer in r=6 feature map

Fig. 4. Optimization of intermediate flow structure of backbone network

Fig. 5. Structure of channel attention module

Fig. 6. Part of training samples

Fig. 7. Basic structure of deep convolutional generative confrontation network

Fig. 8. Changes in different scenarios and time periods. (a) Scene 1; (b) scene 2

Fig. 9. Accuracy curve and loss curve of DeepLabv3+ network. (a) Accuracy curve; (b) loss curve

Fig. 10. Accuracy curve and loss curve of improved DeepLabv3+ network. (a) Accuracy curve; (b) loss curve

Fig. 11. Change detection results of DeepLabv3+ (left) and improved DeepLabv3+ (right). (a) Scene 1; (b) scene 2

Fig. 12. Landsat 8 test images. (a) Scene 3; (b) scene 4

Fig. 13. DeepLabv3+ (left) and improved DeepLabv3+ (right) detection results. (a) Scene 3; (b) scene 4

Fig. 14. Part of images of OSCD dataset. (a) Scene 5; (b) scene 6

Fig. 15. DeepLabv3+ (left) and improved DeepLabv3+ (right) change detection results. (a) Scene 5; (b) scene 6