1. [1] A. Plaza, J. Benediktsson, et al, "Recent advances in techniques for hyperspectral image processing", Remote sensing of enviroment, vol. 113, pp. 110-122, 2009. [ DOI:10.1016/j.rse.2007.07.028] 2. [2] F. Melgani, and L. Bruzzone, "Classification of Hyperspectral Remote Sensing Images With Support Vector Machines". IEEE transactions on geoscience and remote sensing, Vol. 42, pp. 1778-1790, 2004. [ DOI:10.1109/TGRS.2004.831865] 3. [3] 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, pp. 2094-2107, June 2014. [ DOI:10.1109/JSTARS.2014.2329330] 4. [4] T. Li, J. Zhang, X. Zhao, and Y. Zhang, "Classification of hyperspectral image based on deep belief networks," in 2014 IEEE International Conference on Image Processing (ICIP), pp. 1-5, Oct 2014. [ DOI:10.1109/ICIP.2014.7026039] 5. [5] 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, pp. 4544-4554, Aug 2016. [ DOI:10.1109/TGRS.2016.2543748] 6. [6] X. Zhou, S. Li, F. Tang, K. Qin, S. Hu, and S. Liu, "Deep learning with grouped features for spatial spectral classification of hyperspectral images," IEEE Geoscience and Remote Sensing Letters, vol. 14, pp. 97-101, Jan 2017. [ DOI:10.1109/LGRS.2016.2630045] 7. [7] W. Li, G. Wu, F. Zhang, and Q. Du, "Hyperspectral image classification using deep pixel-pair features," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, pp. 844-853, Feb 2017. [ DOI:10.1109/TGRS.2016.2616355] 8. [8] H. Teffahi, H. Yao, S. Chaib, and N. Belabid, "A novel spectral-spatial classification technique for multispectral images using extended multi-attribute profiles and sparse autoencoder," Remote Sensing Letters, vol. 10, no. 1, pp. 30-38, 2019. [ DOI:10.1080/2150704X.2018.1523581] 9. [9] Z. Lin, Y. Chen, X. Zhao, and G. Wang, "Spectral-spatial classification of hyperspectral image using autoencoders," in 2013 9th International Conference on Information, Communications Signal Processing, pp. 1-5, Dec 10. [10] L. Shu, K. McIsaac, and G. R. Osinski, "Hyperspectral image classification with stacking spectral patches and convolutional neural networks," IEEE Transactions on Geoscience and Remote Sensing, pp. 1-10, 2018. [ DOI:10.1109/TGRS.2018.2829400] 11. [11] W. Hu, Y. Huang, L. Wei, F. Zhang, and H. Li, "Deep convolutional neural netoworks for hyperspectral image classification," Journal of Sensors, vol. 2015, no. 3, p. 12pages, 2015. [ DOI:10.1155/2015/258619] 12. [12] P. H. Swain, S. B. Vardeman, and J. C. Tilton, "Contextual classification of multispectral image data," Pattern Recognition, vol. 13, no. 6, pp. 429 - 441, 1981. [ DOI:10.1016/0031-3203(81)90005-4] 13. [13] 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, pp. 6232-6251, Oct 2016. [ DOI:10.1109/TGRS.2016.2584107] 14. [14] 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), pp. 4959-4962, July 2015. [ DOI:10.1109/IGARSS.2015.7326945] 15. [15] S. K. Roy, G. Krishna, S. R. Dubey and B. B. Chaudhuri, "HybridSN: Exploring 3-D-2-D CNN Feature Hierarchy for Hyperspectral Image Classification," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 2, pp. 277-281, Feb. 2020, doi: 10.1109/LGRS.2019.2918719. [ DOI:10.1109/LGRS.2019.2918719] 16. [16] I. Goodfellow, Y. Bengio, and A. Courville. "Deep Learning". MIT Press, 2016. 17. [17] C. Szegedy, Wei L., Yangqing J., P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. "Going deeper with convolutions". In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-9, 2015 [ DOI:10.1109/CVPR.2015.7298594] 18. [18] K. Sohn, H. Lee, X. Yan, "Learning structured output representation using deep conditional generative models, in: Proceedings of the Advances in Neural Information Processing Systems" (NIPS), 2015, pp. 3483- 19. [19] K. O'Shea and R. Nash. "An introduction to convolutional neural networks".CoRR, abs/1511.08458, 2015. 20. [20] Yanming Guo, Yu Liu, Ard Oerlemans, Songyang Lao, Song Wu and Michael S. Lew, "Deep learning for visual understanding: A review, Neurocomputing", http://dx.doi.org/10.1016/j.neucom.2015.09.116 [ DOI:10.1016/j.neucom.2015.09.116] 21. [21] S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi and J. A. Benediktsson, "Deep Learning for Hyperspectral Image Classification: An Overview," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6690-6709, Sept. 2019, doi: 10.1109/TGRS.2019.2907932. [ DOI:10.1109/TGRS.2019.2907932] 22. [22] A. N. Abbasi and M. He, "Convolutional Neural Network with PCA and Batch Normalization for Hyperspectral Image Classification," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 959-962, doi: 10.1109/IGARSS.2019.8899329. [ DOI:10.1109/IGARSS.2019.8899329] 23. [23] "A Deep Learning approach to Hyperspectral Image Classification using an improved Hybrid 3D-2D Convolutional Neural Network" September 2020 Pages 85-92. [ DOI:10.1145/3411408.3411462] 24. [24] S. Ghaderizadeh, D. Abbasi-Moghadam, A. Sharifi, N. Zhao and A. Tariq, "Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 7570-7588, 2021, doi: 10.1109/JSTARS.2021.3099118. [ DOI:10.1109/JSTARS.2021.3099118] 25. [25] A. Ben Hamida et al. "3-D Deep Learning Approach for Remote Sensing Image Classification". In: IEEE Transactions on Geoscience and Remote Sensing 56.8 (2018), pp. 4420-44 2013. [ DOI:10.1109/TGRS.2018.2818945]
|