References

• Arikumar, K.S., Deepak Kumar, A., Gadekallu, T.R., Prathiba, S.B., and Tamilarasi, K. 2022. “Real-time 3D object detection and classification in autonomous driving environment using 3D LiDAR and camera sensors.” Electronics, Vol. 11(No. 24): p. 4203. doi:10.3390/electronics11244203.
   Web of Science ®Google Scholar
• Audebert, N., Le Saux, B., and Lefevre, S. 2019. “Deep learning for classification of hyperspectral data: A comparative review.” IEEE Geoscience and Remote Sensing Magazine, Vol. 7(No. 2): pp. 159–173. doi:10.1109/MGRS.2019.2912563.
   Web of Science ®Google Scholar
• Bashir, M.F., Javed, A.R., Arshad, M.U., Gadekallu, T.R., Shahzad, W., and Beg, M.O. 2023. “Context aware emotion detection from low resource Urdu language using deep neural network.” ACM Transactions on Asian and Low-Resource Language Information Processing, Vol. 22 (No. 5): pp. 1–30. doi:10.1145/3528576.
   Web of Science ®Google Scholar
• Ben Hamida, A., Benoit, A., Lambert, P., and Ben Amar, C. 2018. “3-D deep learning approach for remote sensing image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 56(No. 8): pp. 4420–4434. doi:10.1109/TGRS.2018.2818945.
   Web of Science ®Google Scholar
• Benediktsson, J.A., Palmason, J.A., and Sveinsson, J.R. 2005. “Classification of hyperspectral data from urban areas based on extended morphological profiles.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 43(No. 3): pp. 480–491. doi:10.1109/TGRS.2004.842478.
   Web of Science ®Google Scholar
• Cao, X., Zhou, F., Xu, L., Meng, D., Xu, Z., and Paisley, J. 2018. “Hyperspectral image classification with Markov random fields and a convolutional neural network.” IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society, Vol. 27(No. 5): pp. 2354–2367. doi:10.1109/TIP.2018.2799324.
   PubMed Web of Science ®Google Scholar
• Chen, Y., Chen, Z., Guo, D., Zhao, Z., Lin, T., and Zhang, C. 2022. “Underground space use of urban built-up areas in the central city of Nanjing: Insight based on a dynamic population distribution.” Underground Space., Vol. 7(No. 5): pp. 748–766. doi:10.1016/j.undsp.2021.12.006.
   Web of Science ®Google Scholar
• Chen, Y., Jiang, H., Li, C., Jia, X., and Ghamisi, P. 2016. “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. doi:10.1109/TGRS.2016.2584107.
   Web of Science ®Google Scholar
• Dalla Mura, M., Villa, A., Benediktsson, J.A., Chanussot, J., and Bruzzone, L. 2011. “Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis.” IEEE Geoscience and Remote Sensing Letters, Vol. 8(No. 3): pp. 542–546. doi:10.1109/LGRS.2010.2091253.
   Web of Science ®Google Scholar
• Deng, F., Pu, S., Chen, X., Shi, Y., Yuan, T., and Pu, S. 2018. “Hyperspectral image classification with capsule network using limited training samples.” Sensors (Basel, Switzerland), Vol. 18(No. 9): p. 3153. doi:10.3390/s18093153.
   PubMed Web of Science ®Google Scholar
• El-Assal, M., Tirilly, P., and Bilasco, I.M. 2022. 2D versus 3D convolutional spiking neural networks trained with unsupervised STDP for human action recognition. http://arxiv.org/abs/2205.13474
   Google Scholar
• Fang, B., Liu, Y., Zhang, H., and He, J. 2022. “Hyperspectral image classification based on 3D asymmetric inception network with data fusion transfer learning.” Remote Sensing, Vol. 14(No. 7): p. 1711. doi:10.3390/rs14071711.
   Web of Science ®Google Scholar
• Fang, L., He, N., Li, S., Ghamisi, P., and Benediktsson, J.A. 2018. “Extinction Profiles Fusion for Hyperspectral Images Classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 56(No. 3): pp. 1803–1815. doi:10.1109/TGRS.2017.2768479.
   Web of Science ®Google Scholar
• Fang, L., Liu, G., Li, S., Ghamisi, P., and Benediktsson, J.A. 2019. “Hyperspectral image classification with squeeze multibias network.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 3): pp. 1291–1301. doi:10.1109/TGRS.2018.2865953.
   Web of Science ®Google Scholar
• Fu, C., Yuan, H., Xu, H., Zhang, H., and Shen, L. 2023. “TMSO-Net: Texture adaptive multiscale observation for light field image depth estimation.” Journal of Visual Communication and Image Representation, Vol. 90: p. 103731. doi:10.1016/j.jvcir.2022.103731.
   Web of Science ®Google Scholar
• Gadekallu, T.R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P.K.R., and Srivastava, G. 2023. “Deep neural networks to predict diabetic retinopathy.” Journal of Ambient Intelligence and Humanized Computing, Vol. 14(No. 5): pp. 5407–5420. doi:10.1007/s12652-020-01963-7.
   Google Scholar
• Garg, H., Gupta, N., Agrawal, R., Shivani, S., and Sharma, B. 2022. “A real time cloud-based framework for glaucoma screening using EfficientNet.” Multimedia Tools and Applications, Vol. 81 (No. 24): pp. 34737–34758. doi:10.1007/s11042-021-11559-8.
   Web of Science ®Google Scholar
• Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., and Plaza, A. 2017. “Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art.” IEEE Geoscience and Remote Sensing Magazine, Vol. 5(No. 4): pp. 37–78. doi:10.1109/MGRS.2017.2762087.
   Web of Science ®Google Scholar
• Glorot, X., and Bengio, Y. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249–256).
   Google Scholar
• Grupo de Inteligencia Computacional (GIC). 2023. Ehu.eus website: https://www.ehu.eus/ccwintco/index.php
   Google Scholar
• Guo, Z., Yu, K., Li, Y., Srivastava, G., and Lin, J.C.-W. 2022. “Deep learning-embedded social internet of things for ambiguity-aware social recommendations.” IEEE Transactions on Network Science and Engineering, Vol. 9(No. 3): pp. 1067–1081. doi:10.1109/TNSE.2021.3049262.
   Web of Science ®Google Scholar
• Hang, R., Liu, Q., Hong, D., and Ghamisi, P. 2019. “Cascaded recurrent neural networks for hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 8): pp. 5384–5394. doi:10.1109/TGRS.2019.2899129.
   Web of Science ®Google Scholar
• He, X., and Chen, Y. 2021. “Modifications of the multi-layer perceptron for hyperspectral image classification.” Remote Sensing, Vol. 13(No. 17): p. 3547. doi:10.3390/rs13173547.
   Web of Science ®Google Scholar
• Hong, D., Han, Z., Yao, J., Gao, L., Zhang, B., Plaza, A., and Chanussot, J. 2022. “SpectralFormer: Rethinking hyperspectral image classification with transformers.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–16. doi:10.1109/TGRS.2021.3130716.
   Web of Science ®Google Scholar
• Ji, J., Liu, S., Zhang, F., Liao, X., Wang, S., and Liao, J. 2023. “Hyperspectral Image Classification Based on Unsupervised Regularization.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 16: pp. 1871–1882. doi:10.1109/JSTARS.2023.3241662.
   Web of Science ®Google Scholar
• Jiang, Z., and Chen, L. 2022. “Multisemantic Level Patch Merger Vision Transformer for diagnosis of pneumonia.” Computational and Mathematical Methods in Medicine, Vol. 2022: pp. 7852958. doi:10.1155/2022/7852958.
   PubMed Web of Science ®Google Scholar
• Joelsson, S.R., Benediktsson, J.A., and Sveinsson, J.R. 2005. Random forest classifiers for hyperspectral data. In Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. IEEE. doi:10.1109/IGARSS.2005.1526129.
   Google Scholar
• Li, R., Zheng, S., Duan, C., Yang, Y., and Wang, X. 2020. “Classification of hyperspectral image based on double-branch dual-attention mechanism network.” Remote Sensing, Vol. 12(No. 3): pp. 582. doi:10.3390/rs12030582.
   Web of Science ®Google Scholar
• Linzen, T., Dupoux, E., and Goldberg, Y. 2016. “Assessing the ability of LSTMs to learn syntax-sensitive dependencies.” Transactions of the Association for Computational Linguistics, Vol. 4: pp. 521–535. doi:10.1162/tacl_a_00115.
   Google Scholar
• Melgani, F., and Bruzzone, L. 2004. “Classification of hyperspectral remote sensing images with support vector machines.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 42(No. 8): pp. 1778–1790. doi:10.1109/TGRS.2004.831865.
   Web of Science ®Google Scholar
• Mou, L., and Zhu, X.X. 2020. “Learning to pay attention on spectral domain: A spectral attention module-based convolutional network for hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 58(No. 1): pp. 110–122. doi:10.1109/TGRS.2019.2933609.
   Web of Science ®Google Scholar
• Mou, L., Lu, X., Li, X., and Zhu, X.X. 2020. “Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 58(No. 12): pp. 8246–8257. doi:10.1109/TGRS.2020.2973363.
   Web of Science ®Google Scholar
• Pan, B., Shi, Z., and Xu, X. 2017. “R-VCANet: A new deep-learning-based hyperspectral image classification method.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10(No. 5): pp. 1975–1986. doi:10.1109/JSTARS.2017.2655516.
   Web of Science ®Google Scholar
• Peng, Y., Zhang, Y., Tu, B., Li, Q., and Li, W. 2022. “Spatial–Spectral Transformer With Cross-Attention for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–15. doi:10.1109/TGRS.2022.3203476.
   Google Scholar
• Ravi, C., Tigga, A., Reddy, G.T., Hakak, S., and Alazab, M. 2022. “Driver identification using optimized deep learning model in smart transportation.” ACM Transactions on Internet Technology, Vol. 22(No. 4): pp. 1–17. doi:10.1145/3412353.
   Web of Science ®Google Scholar
• Roy, S.K., Krishna, G., Dubey, S.R., and Chaudhuri, B.B. 2020. “HybridSN: Exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification.” IEEE Geoscience and Remote Sensing Letters, Vol. 17(No. 2): pp. 277–281. doi:10.1109/LGRS.2019.2918719.
   Web of Science ®Google Scholar
• Saab, S., Jr, Phoha, S., Zhu, M., and Ray, A. 2022. “An adaptive polyak heavy-ball method.” Machine Learning, Vol. 111 (No. 9): pp. 3245–3277. doi:10.1007/s10994-022-06215-7.
   Web of Science ®Google Scholar
• Sharma, M., and Biswas, M. 2018. “CRCOED: Collaborative representation-based classification using odd even decomposition for hyperspectral remote sensing imagery.” Procedia Computer Science, Vol. 143: pp. 458–465. doi:10.1016/j.procs.2018.10.418.
   Google Scholar
• Sun, Y., Fu, Z., and Fan, L. 2019. “A novel hyperspectral image classification pattern using random patches convolution and local covariance.” Remote Sensing, Vol. 11(No. 16): p. 1954. doi:10.3390/rs11161954.
   Web of Science ®Google Scholar
• Vallathan, G., John, A., Thirumalai, C., Mohan, S., Srivastava, G., and Lin, J.C.-W. 2021. “Suspicious activity detection using deep learning in secure assisted living IoT environments.” The Journal of Supercomputing, Vol. 77(No. 4): pp. 3242–3260. doi:10.1007/s11227-020-03387-8.
   Web of Science ®Google Scholar
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. 2017. Attention is all you need. http://arxiv.org/abs/1706.03762
   Google Scholar
• Wang, S., Hu, X., Sun, J., and Liu, J. 2023. “Hyperspectral anomaly detection using ensemble and robust collaborative representation.” Information Sciences, Vol. 624: pp. 748–760. doi:10.1016/j.ins.2022.12.096.
   Web of Science ®Google Scholar
• Waske, B., van der Linden, S., Benediktsson, J.A., Rabe, A., and Hostert, P. 2010. “Sensitivity of support vector machines to random feature selection in classification of hyperspectral data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 48(No. 7): pp. 2880–2889. doi:10.1109/TGRS.2010.2041784.
   Web of Science ®Google Scholar
• Xie, S., Girshick, R., Dollar, P., Tu, Z., and He, K. 2017. Aggregated residual transformations for deep neural networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. doi:10.1109/CVPR.2017.634.
   Google Scholar
• Xu, X., Shen, B., Ding, S., Srivastava, G., Bilal, M., Khosravi, M.R., Menon, V.G., Jan, M.A., and Wang, M. 2022. “Service offloading with deep Q-network for digital twinning-empowered internet of vehicles in edge computing.” IEEE Transactions on Industrial Informatics, Vol. 18(No. 2): pp. 1414–1423. doi:10.1109/TII.2020.3040180.
   Web of Science ®Google Scholar
• Yadav, D.P., Jalal, A.S., and Prakash, V. 2022. “Human burn depth and grafting prognosis using ResNeXt topology based deep learning network.” Multimedia Tools and Applications, Vol. 81(No. 13): pp. 18897–18914. doi:10.1007/s11042-022-12555-2.
   Web of Science ®Google Scholar
• Zhao, B., Ragnarsson, H.I., Ulfarsson, M.O., Cavallaro, G., and Benediktsson, J.A. 2022. “Predicting Classification Performance for Benchmark Hyperspectral Datasets.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15: pp. 4180–4193. doi:10.1109/JSTARS.2022.3173893.
   Google Scholar
• Zhuo, Z., Du, L., Lu, X., Chen, J., and Cao, Z. 2022. “Smoothed lv distribution based three-dimensional imaging for spinning space debris.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–13. doi:10.1109/TGRS.2022.3174677.
   Web of Science ®Google Scholar