Acta Optica Sinica, Volume. 40, Issue 21, 2128002(2020)
Multi-Objective Optimization of Hyperspectral Band Selection Based on Attention Mechanism
Figures & Tables(14)
Fig. 3. True color image and ground truth map of Botswana data set. (a) True color image; (b) ground truth map
Fig. 4. True color image and ground truth map of Indian Pines data set. (a) True color image; (b) ground truth map
Fig. 6. Overall classification accuracy, training loss, and band weight changes in the Botswana data set. (a) Overall classification accuracy; (b) training loss; (c) band weight thermal map
Fig. 7. Overall classification accuracy, training loss and band weight changes on the Indian Pines data set. (a) Overall classification accuracy; (b) training loss; (c) band weight thermal map
Fig. 8. Overall classification accuracy, average classification accuracy and Kappa coefficient of each algorithm in the Botswana data set. (a) Overall classification accuracy; (b) average classification accuracy; (c) Kappa coefficient
Fig. 10. Overall classification accuracy, average classification accuracy and Kappa coefficient of each algorithm in the Indian Pines data set. (a) Overall classification accuracy; (b) average classification accuracy; (c) Kappa coefficient
Fig. 11. Average spectral divergence of each algorithm on the Indian Pines data set
Table 1. Data size and activation function change in the model
View tableTable 1. Data size and activation function change in the model
Module Layer Input size Output size Activation Input 1×1×b Attention module FC-1(fully connected layer) 1×1×b 1×1×(b/16) ReLU FC-2(fully connected layer) 1×1×(b/16) 1×1×b Sigmoid Encoder-1(autoencoder) 1×1×b 1×1×256 BN-1(batch normalization) 1×1×256 1×1×256 ReLU Encoder-2(autoencoder) 1×1×256 1×1×128 BN-2(batch normalization) 1×1×128 1×1×128 ReLU Encoder-3(autoencoder) 1×1×128 1×1×64 BN-3(batch normalization) 1×1×64 1×1×64 ReLU Reconstruction module Encoder-4(autoencoder) 1×1×64 1×1×64 BN-4(batch normalization) 1×1×64 1×1×64 ReLU Decoder-1(autoencoder) 1×1×64 1×1×128 BN-5(batch normalization) 1×1×128 1×1×128 ReLU Decoder-2(autoencoder) 1×1×128 1×1×256 BN-6(batch normalization) 1×1×256 1×1×256 ReLU Decoder-3(autoencoder) 1×1×256 1×1×b Sigmoid Classification module Latent vector 1×1×64 1×1×64 FC-3(fully connected layer) 1×1×64 Number of class Softmax
Table 2. Hyperspectral image data set
View tableTable 2. Hyperspectral image data set
Item Botswana Indian Pines Shooting area Okavango Delta, Botswana Indiana, USA Imaging spectrometer Hyperion AVIRIS Spectral range /nm 400-2500 400-2500 Number of wavelengths (remove strong noise and water vapor band) 145 200 Image size /(pixel×pixel) 1476×256 145×145 Spatial resolution /m 30 20 Sample size 3248 10249 Object types 14 16
Table 3. Experimental results of two data sets with different weight coefficients
View tableTable 3. Experimental results of two data sets with different weight coefficients
γ Botswana Indian Pines OA /% AA /% Kappa OA /% AA /% Kappa 0.1 88.9 89.5 0.873 73.1 71.4 0.708 0.3 89.3 89.8 0.886 73.6 71.5 0.706 0.5 88.6 87.1 0.869 74.3 70.4 0.712 0.7 87.2 86.8 0.853 72.1 69.5 0.698 0.9 85.3 86.7 0.839 69.7 66.1 0.664
Tools
Get Citation
Copy Citation Text
Shihao Guan, Guang Yang, Shan Lu, Yanyu Fu. Multi-Objective Optimization of Hyperspectral Band Selection Based on Attention Mechanism[J]. Acta Optica Sinica, 2020, 40(21): 2128002
Paper Information
Category: Remote Sensing and Sensors
Received: Jun. 30, 2020
Accepted: Jul. 20, 2020
Published Online: Oct. 26, 2020
The Author Email: Yang Guang (1026269743@qq.com)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20