IEEE Geoscience and Remote Sensing Magazine - June 2022 - 288

image classification, " IEEE Trans. Geosci. Remote Sens., vol. 58,
no. 5, pp. 3162-3177, 2020, doi: 10.1109/TGRS.2019.2949180.
[81] G. E. Hinton, S. Osindero, and Y. Teh, " A fast learning algorithm
for deep belief nets, " Neural Comput., vol. 18, no. 7, pp. 1527-
1554, 2006, doi: 10.1162/neco.2006.18.7.1527.
[82] W. Diao, X. Sun, X. Zheng, F. Dou, H. Wang, and K. Fu, " Efficient
saliency-based object detection in remote sensing images
using deep belief networks, " IEEE Geosci. Remote Sens. Lett., vol.
13, no. 2, pp. 137-141, 2016, doi: 10.1109/LGRS.2015.2498644.
[83] Q. Zou, L. Ni, T. Zhang, and Q. Wang, " Deep learning-based
feature selection for remote sensing scene classification, " IEEE
Geosci. Remote Sens. Lett., vol. 12, no. 11, pp. 2321-2325, 2015,
doi: 10.1109/LGRS.2015.2475299.
[84] X. Zhang, Y. Liang, C. Li, N. Huyan, L. Jiao, and H. Zhou, " Recursive
autoencoders-based unsupervised feature learning
for hyperspectral
image classification, " IEEE Geosci. Remote
Sens. Lett., vol. 14, no. 11, pp. 1928-1932, 2017, doi: 10.1109/
LGRS.2017.2737823.
[85] P. Zhou, J. Han, G. Cheng, and B. Zhang, " Learning compact
and discriminative stacked autoencoder for hyperspectral image
classification, " IEEE Trans. Geosci. Remote Sens., vol. 57, no.
7, pp. 4823-4833, 2019, doi: 10.1109/TGRS.2019.2893180.
[86] S. Mei, J. Ji, Y. Geng, Z. Zhang, X. Li, and Q. Du, " Unsupervised
spatial spectral feature learning by 3d convolutional autoencoder
for hyperspectral classification, " IEEE Trans. Geosci.
Remote Sens., vol. 57, no. 9, pp. 6808-6820, 2019, doi: 10.1109/
TGRS.2019.2908756.
[87] X. Wang, K. Tan, Y. Chen, and Q. Du, " Hyperspectral image
classification based on 3-d octave convolution with spatial
spectral attention network, " IEEE Trans. Geosci. Remote Sens.,
vol. 59, no. 3, pp. 2430-2447, 2021, doi: 10.1109/TGRS.2020.
3005431.
[88] F. Zhang, B. Du, and L. Zhang, " Saliency-guided unsupervised
feature learning for scene classification, " IEEE Trans. Geosci. Remote
Sens., vol. 53, no. 4, pp. 2175-2184, 2015, doi: 10.1109/
TGRS.2014.2357078.
[89] Y. Su, J. Li, A. Plaza, A. Marinoni, P. Gamba, and S. Chakravortty,
" Daen: Deep autoencoder networks for hyperspectral
unmixing, " IEEE Trans. Geosci. Remote Sens., vol. 57, no. 7, pp.
4309-4321, 2019, doi: 10.1109/TGRS.2018.2890633.
[90] Y. Qu and H. Qi, " UDAS: An untied denoising autoencoder
with sparsity for spectral unmixing, " IEEE Trans. Geosci. Remote
Sens., vol. 57, no. 3, pp. 1698-1712, 2019, doi: 10.1109/
TGRS.2018.2868690.
[91] Y. Su, A. Marinoni, J. Li, J. Plaza, and P. Gamba, " Stacked nonnegative
sparse autoencoders for robust hyperspectral unmixing, "
IEEE Geosci. Remote Sens. Lett., vol. 15, no. 9, pp. 1427-
1431, 2018, doi: 10.1109/LGRS.2018.2841400.
[92] F. Khajehrayeni and H. Ghassemian, " Hyperspectral unmixing
using deep convolutional autoencoders in a supervised scenario, "
IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 13, pp.
567-576, Feb. 2020, doi: 10.1109/JSTARS.2020.2966512.
[93] B. Palsson, M. O. Ulfarsson, and J. R. Sveinsson, " Convolutional
autoencoder for spectral-spatial hyperspectral unmixing, " IEEE
Trans. Geosci. Remote Sens., vol. 59, no. 1, pp. 535-549, 2021, doi:
10.1109/TGRS.2020.2992743.
288
[94] I. Goodfellow et al., " Generative adversarial nets, " in Proc. Adv
Neural Inf. Process. Syst., 2014, pp. 2672-2680.
[95] Y. Zhan, D. Hu, Y. Wang, and X. Yu, " Semisupervised hyperspectral
image classification based on generative adversarial
networks, " IEEE Geosci. Remote Sens. Lett., vol. 15, no. 2, pp.
212-216, 2018, doi: 10.1109/LGRS.2017.2780890.
[96] J. Feng, H. Yu, L. Wang, X. Cao, X. Zhang, and L. Jiao, " Classification
of hyperspectral images based on multiclass spatial
spectral generative adversarial networks, " IEEE Trans. Geosci.
Remote Sens., vol. 57, no. 8, pp. 5329-5343, 2019, doi: 10.1109/
TGRS.2019.2899057.
[97] Q. Xiao, B. Liu, Z. Li, W. Ni, Z. Yang, and L. Li, " Progressive
data augmentation method for remote sensing ship image
classification based on imaging simulation system and neural
style transfer, " IEEE J. Sel. Topics Appl. Earth Observ. Remote
Sens., vol. 14, pp. 9176-9186, Sep. 2021, doi: 10.1109/
JSTARS.2021.3109600.
[98] R. Hang, F. Zhou, Q. Liu, and P. Ghamisi, " Classification of
hyperspectral images via multitask generative adversarial networks, "
IEEE Trans. Geosci. Remote Sens., vol. 59, no. 2, pp. 1424-
1436, 2021, doi: 10.1109/TGRS.2020.3003341.
[99] H. Chen, W. Li, and Z. Shi, " Adversarial instance augmentation
for building change detection in remote sensing images, " IEEE
Trans. Geosci. Remote Sens., vol. 60, pp. 1-16, Mar. 2021, doi:
10.1109/TGRS.2021.3066802.
[100] Y. Yu, X. Li, and F. Liu, " Attention GANs: Unsupervised deep
feature learning for aerial scene classification, " IEEE Trans. Geosci.
Remote Sens., vol. 58, no. 1, pp. 519-531, 2020, doi: 10.1109/
TGRS.2019.2937830.
[101] C. Du, C. Huo, L. Zhang, B. Chen, and Y. Yuan, " Fast CW: A fast
adversarial attack algorithm to fool SAR target recognition with
deep convolutional neural networks, " IEEE Geosci. Remote Sens.
Lett., vol. 19, Feb. 2021, doi: 10.1109/LGRS.2021.3058011.
[102] W. Xie, Y. Cui, Y. Li, J. Lei, Q. Du, and J. Li, " HPGAN: Hyperspectral
pansharpening using 3-d generative adversarial
networks, " IEEE Trans. Geosci. Remote Sens., vol. 59, no. 1, pp.
463-477, 2021, doi: 10.1109/TGRS.2020.2994238.
[103] K. Jiang, Z. Wang, P. Yi, G. Wang, T. Lu, and J. Jiang, " Edge-enhanced
GAN for remote sensing image superresolution, " IEEE
Trans. Geosci. Remote Sens., vol. 57, no. 8, pp. 5799-5812, 2019,
doi: 10.1109/TGRS.2019.2902431.
[104] M. F. Reyes, S. Auer, N. Merkle, C. Henry, and M. Schmitt, " Sarto-optical
image translation based on conditional generative
adversarial networks optimization, opportunities and limits, "
Remote Sens., vol. 11, no. 17, p. 2067, 2019, doi: 10.3390/
rs11172067.
[105] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, " Learning
representations by back-propagating errors, " Nature, vol. 323,
no. 6088, pp. 533-536, 1986, doi: 10.1038/323533a0.
[106] H. Wu and P. Saurabh, " Convolutional recurrent neural networks
for hyperspectral data classification, " Remote Sens., vol.
9, no. 3, p. 298, 2017, doi: 10.3390/rs9030298.
[107] R. Hang, Q. Liu, D. Hong, and P. Ghamisi, " Cascaded recurrent
neural networks for hyperspectral image classification, " IEEE
Trans. Geosci. Remote Sens., vol. 57, no. 8, pp. 5384-5394, 2019,
doi: 10.1109/TGRS.2019.2899129.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2022
IEEE Geoscience and Remote Sensing Magazine - June 2022

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2022
