Fig. 1. Two-dimensional and three-dimensional convolution network diagrams. (a) 2D-CNN; (b) 3D-CNN

Fig. 2. Normal and dilated convolution kernel diagrams. (a) Normal kernel; (b) dilated kernel (r=2)

Fig. 3. Seven permutation and combination types of dilated and normal convolutional layers and two activation function distribution strategies. (a) Type 1; (b) type 2; (c) type 3; (d) type 4; (e) type 5; (f) type 6; (g) type 7; (h) distribution strategy Ⅰ; (i) distribution strategy Ⅱ

Fig. 4. Receptive field's distributions of different convolution combinations. (a) Dilation parameter distribution is (2,2,2); (b) dilation parameter distribution is (1,2,2); (c) dilation parameter distribution is (1,1,2)

Fig. 5. Network structure

Fig. 6. Corresponding precision of dilation rate in two datasets. (a) Indian Pines spectral dimension; (b) Salinas spectral dimension; (c) Indian Pines spatial dimension; (d) Salinas spatial dimension

Fig. 7. Pseudo-color composite images of two datasets. (a) Indian Pines; (b) Salinas

Fig. 8. Classification images of different network models in Indian Pines dataset. (a) True value image; (b) SVM; (c) 2D-CNN; (d) Res-3DCNN; (e) M3D-DCNN; (f) 3D-CNN; (g) Dilated-3D-CNN

Fig. 9. Classification images of different network models in Salinas dataset. (a) True value image; (b) SVM; (c) 2D-CNN; (d) Res-3DCNN; (e) M3D-DCNN; (f) 3D-CNN; (g) Dilated-3D-CNN