Wenhao Chen, Jing He, Gang Liu. Hyperspectral Image Classification Based on Convolution Neural Network with Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1811001
- Laser & Optoelectronics Progress
- Vol. 59, Issue 18, 1811001 (2022)
Fig. 1. Basic structure of convolutional neural network
Fig. 2. Residual structure
Fig. 3. SE module
Fig. 4. SE-Res block
Fig. 5. SE-ResNet structure
Fig. 6. Flase color images. (a) Indian Pines; (b) Pavia University
Fig. 7. Overall accuracy of different reduction ratio
Fig. 8. Overall accuracy of different space sizes
Fig. 9. Loss and accuracy of training and validation sets of Indian Pines with different sample ratios. (a) 1∶1∶8; (b) 2∶1∶7; (c) 3∶1∶6; (d) 4∶1∶5
Fig. 10. Classification maps for Indian Pines. (a) Ground truth; (b) SVM; (c) 2D-CNN; (d) 3D-CNN; (e) ResNet; (f) SE-ResNet
Fig. 11. Classification maps for Pavia University. (a) Ground truth; (b) SVM; (c) 2D-CNN; (d) 3D-CNN; (e) ResNet; (f) SE-ResNet
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Table 1. Classification accuracy and training time of Indian Pines and Pavia University datasets with different sample ratios
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Table 2. Classification accuracy of Indian Pines and Pavia University datasets
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