TY - JOUR
T1 - Immune Evolutionary Generative Adversarial Networks for Hyperspectral Image Classification
AU - Bai, Jing
AU - Zhang, Yang
AU - Xiao, Zhu
AU - Ye, Fawang
AU - Li, You
AU - Alazab, Mamoun
AU - Jiao, Licheng
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - In recent years, hyperspectral image classification (HIC) algorithm based on deep learning has been widely studied, and has achieved much better results than traditional algorithms. HIC using small samples has gradually become a research hotspot, and the generative adversarial networks (GANs) have become a brilliant application in this field. However, the HIC results based on GAN methods are poor and volatile, since a single loss function cannot accurately measure the distance between the generated samples and the real samples in different hyperspectral images. To resolve this problem, we propose a novel immune evolutionary generative adversarial network (HIEGAN) via leveraging the evolutionary strategy and immune strategy. Specifically, we enhance the performance of the generator in two ways: 1) HIEGAN uses multiple loss functions for calculation and backpropagation, so as to endow the generator with different parameter values and select the best one as the evolution result each time to enter the next iteration and 2) in the training process, we preserve the optimal generator as memory cells to avoid the performance degradation of the generator. Through these changes, HIEGAN overcame the defects of GAN, improved the stability of GAN, and finally improved classification efficiency. At the same time, in order to alleviate the overfitting problem of depth network under small samples, we change convolution and deconvolution into ghost module to reduce the network parameters. Experiments on three classical datasets validate that HIEGAN has encouraging performance in HIC under small samples.
AB - In recent years, hyperspectral image classification (HIC) algorithm based on deep learning has been widely studied, and has achieved much better results than traditional algorithms. HIC using small samples has gradually become a research hotspot, and the generative adversarial networks (GANs) have become a brilliant application in this field. However, the HIC results based on GAN methods are poor and volatile, since a single loss function cannot accurately measure the distance between the generated samples and the real samples in different hyperspectral images. To resolve this problem, we propose a novel immune evolutionary generative adversarial network (HIEGAN) via leveraging the evolutionary strategy and immune strategy. Specifically, we enhance the performance of the generator in two ways: 1) HIEGAN uses multiple loss functions for calculation and backpropagation, so as to endow the generator with different parameter values and select the best one as the evolution result each time to enter the next iteration and 2) in the training process, we preserve the optimal generator as memory cells to avoid the performance degradation of the generator. Through these changes, HIEGAN overcame the defects of GAN, improved the stability of GAN, and finally improved classification efficiency. At the same time, in order to alleviate the overfitting problem of depth network under small samples, we change convolution and deconvolution into ghost module to reduce the network parameters. Experiments on three classical datasets validate that HIEGAN has encouraging performance in HIC under small samples.
KW - Generative adversarial networks (GAN)
KW - Hyperspectral image classification
KW - Immune evolutionary algorithm
UR - http://www.scopus.com/inward/record.url?scp=85139510329&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2022.3210280
DO - 10.1109/TGRS.2022.3210280
M3 - Article
AN - SCOPUS:85139510329
VL - 60
SP - 1
EP - 15
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
M1 - 5543614
ER -