1. [1] W. Zhao and S. Du, " Learning multiscale and deep representations for classifying remotely sensed imagery", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 113, pp. 155-165, Mar. 2016, doi: 10.1016/j.isprsjprs.2016.01.004. [DOI:10.1016/j.isprsjprs.2016.01.004]

2. [2] M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, " Deep learning classifiers for hyperspectral imaging: A review", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 158, pp. 279-317, 2019. [DOI:10.1016/j.isprsjprs.2019.09.006]

3. [3] G. Camps-Valls and L. Bruzzone, " Kernel-based methods for hyperspectral image classification", IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 6, pp. 1351-1362, Jun. 2005, doi: 10.1109/TGRS.2005.846154. [DOI:10.1109/TGRS.2005.846154]

4. [4] G. Hughes, " On the mean accuracy of statistical pattern recognizers", IEEE Transactions on Information Theory, vol. 14, no. 1, pp. 55-63, Jan. 1968, doi: 10.1109/TIT.1968.1054102. [DOI:10.1109/TIT.1968.1054102]

5. [5] L. O. Jimenez and D. A. Landgrebe, " Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data", IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 28, no. 1, pp. 39-54, Feb. 1998, doi: 10.1109/5326.661089. [DOI:10.1109/5326.661089]

6. [6] A. Plaza, J. Plaza, A. Paz, and S. Sanchez, " Parallel Hyperspectral Image and Signal Processing [Applications Corner]", IEEE Signal Processing Magazine, vol. 28, no. 3, pp. 119-126, May 2011, doi: 10.1109/MSP.2011.940409. [DOI:10.1109/MSP.2011.940409]

7. [7] Y. Chen, K. Zhu, L. Zhu, X. He, P. Ghamisi, and J. A. Benediktsson, 'Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification', IEEE Transactions on Geoscience and Remote Sensing, vol. 57, 2019. [DOI:10.1109/TGRS.2019.2910603]

8. [8] F. Hu, G.-S. Xia, Z. Wang, X. Huang, L. Zhang, and H. Sun, " Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 5, pp. 2015-2030, May 2015, doi: 10.1109/JSTARS.2015.2444405. [DOI:10.1109/JSTARS.2015.2444405]

9. [9] Y. Bengio, A. Courville, and P. Vincent, " Representation Learning: A Review and New Perspectives", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, Aug. 2013, doi: 10.1109/TPAMI.2013.50. [DOI:10.1109/TPAMI.2013.50]

10. [10] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, " Deep learning for visual understanding: A review", Neurocomputing, vol. 187, pp. 27-48, Apr. 2016, doi: 10.1016/j.neucom.2015.09.116. [DOI:10.1016/j.neucom.2015.09.116]

11. [11] W. Hu, Y. Huang, and L. Wei, " Deep Convolutional Neural Networks for Hyperspectral Image Classification". [Online]. Available: https://www.hindawi.com/journals/js/2015/258619/. [Accessed: 16-Oct-2019]. [DOI:10.1155/2015/258619]

12. [12] K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis, " Deep supervised learning for hyperspectral data classification through convolutional neural networks", in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 4959-4962, doi: 10.1109/IGARSS.2015.7326945. [DOI:10.1109/IGARSS.2015.7326945]

13. [13] A. Vallet and H. Sakamoto, " A Multi-Label Convolutional Neural Network for Automatic Image Annotation", Journal of Information Processing, vol. 23, pp. 767-775, Nov. 2015, doi: 10.2197/ipsjjip.23.767. [DOI:10.2197/ipsjjip.23.767]

14. [14] Y. Chen, Z. Lin, X. Zhao, G. Wang, and Y. Gu, " Deep Learning-Based Classification of Hyperspectral Data", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2094-2107, Jun. 2014, doi: 10.1109/JSTARS.2014.2329330. [DOI:10.1109/JSTARS.2014.2329330]

15. [15] F. Hu, G.-S. Xia, J. Hu, and L. Zhang, " Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery", Remote Sensing, vol. 7, no. 11, pp. 14680-14707, Nov. 2015, doi: 10.3390/rs71114680. [DOI:10.3390/rs71114680]

16. [16] T. Li, J. Zhang, and Y. Zhang, " Classification of hyperspectral image based on deep belief networks", 2014 IEEE International Conference on Image Processing (ICIP), pp. 5132-5136, 2014, doi: 10.1109/icip.2014.7026039. [DOI:10.1109/ICIP.2014.7026039]

17. [17] Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, " Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks", IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, pp. 6232-6251, Oct. 2016, doi: 10.1109/TGRS.2016.2584107. [DOI:10.1109/TGRS.2016.2584107]

18. [18] M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, " A new deep convolutional neural network for fast hyperspectral image classification", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 120-147, Nov. 2018, doi: 10.1016/j.isprsjprs.2017.11.021. [DOI:10.1016/j.isprsjprs.2017.11.021]

19. [19] W. Zhao and S. Du, " Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach", IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4544-4554, Aug. 2016, doi: 10.1109/TGRS.2016.2543748. [DOI:10.1109/TGRS.2016.2543748]

20. [20] Y. Li, W. Xie, and H. Li, " Hyperspectral image reconstruction by deep convolutional neural network for classification", Pattern Recognition, vol. 63, pp. 371-383, Mar. 2017, doi: 10.1016/j.patcog.2016.10.019. [DOI:10.1016/j.patcog.2016.10.019]

21. [21] A. Romero, C. Gatta, and G. Camps-Valls, " Unsupervised Deep Feature Extraction for Remote Sensing Image Classification", p. 21, 2015. [DOI:10.1109/WHISPERS.2014.8077647]

22. [22] Z. Deng, H. Sun, S. Zhou, J. Zhao, L. Lei, and H. Zou, " Multi-scale object detection in remote sensing imagery with convolutional neural networks", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 3-22, Nov. 2018, doi: 10.1016/j.isprsjprs.2018.04.003. [DOI:10.1016/j.isprsjprs.2018.04.003]

23. [23] P. Ghamisi, Y. Chen, and X. X. Zhu, " A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data", IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 10, pp. 1537-1541, Oct. 2016, doi: 10.1109/LGRS.2016.2595108. [DOI:10.1109/LGRS.2016.2595108]

24. [24] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, " Gradient-based learning applied to document recognition", Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998, doi: 10.1109/5.726791. [DOI:10.1109/5.726791]

25. [25] M. D. Zeiler and R. Fergus, " Visualizing and Understanding Convolutional Networks", in Computer Vision - ECCV 2014, vol. 8689, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, pp. 818-833. [DOI:10.1007/978-3-319-10590-1_53]

26. [26] J. Gu et al., " Recent advances in convolutional neural networks", Pattern Recognition, vol. 77, pp. 354-377, May 2018, doi: 10.1016/j.patcog.2017.10.013. [DOI:10.1016/j.patcog.2017.10.013]

27. [27] H.-J. Yoo, " Deep Convolution Neural Networks in Computer Vision: a Review", IEIE Transactions on Smart Processing and Computing, vol. 4, pp. 35-43, Feb. 2015, doi: 10.5573/IEIESPC.2015.4.1.035. [DOI:10.5573/IEIESPC.2015.4.1.035]

28. [28] M. E. Paoletti et al., " Capsule Networks for Hyperspectral Image Classification", IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 57, no. 4, pp. 2145-2160, 2018. [DOI:10.1109/TGRS.2018.2871782]

29. [29] T.Alipour-Fard, H. Arefi, " Structure Aware Generative Adversarial Networks for Hyperspectral Image Classification", JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , vol. 13, pp. 5424-5438, 2020. [DOI:10.1109/JSTARS.2020.3022781]

30. [30] G. Sumbul et al., " Bigearthnet: A large-scale benchmark archive for remote sensing image understanding", IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, pp. 5901-5904, 2019. [DOI:10.1109/IGARSS.2019.8900532]

31. [31] J. Li, K. Zheng, J. Yao, L. Gao and D. Hong, " Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion", IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022. [DOI:10.1109/LGRS.2022.3151779]