{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:27:37Z","timestamp":1783121257647,"version":"3.54.6"},"reference-count":69,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T00:00:00Z","timestamp":1600905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australia Research Council Grant","award":["DP150100294 and DP150104251"],"award-info":[{"award-number":["DP150100294 and DP150104251"]}]},{"DOI":"10.13039\/501100000980","name":"Grains Research and Development Corporation","doi-asserted-by":"publisher","award":["UWA2002-003RTX"],"award-info":[{"award-number":["UWA2002-003RTX"]}],"id":[{"id":"10.13039\/501100000980","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we propose a high performance Two-Stream spectral-spatial Residual Network (TSRN) for hyperspectral image classification. The first spectral residual network (sRN) stream is used to extract spectral characteristics, and the second spatial residual network (saRN) stream is concurrently used to extract spatial features. The sRN uses 1D convolutional layers to fit the spectral data structure, while the saRN uses 2D convolutional layers to match the hyperspectral spatial data structure. Furthermore, each convolutional layer is preceded by a Batch Normalization (BN) layer that works as a regularizer to speed up the training process and to improve the accuracy. We conducted experiments on three well-known hyperspectral datasets, and we compare our results with five contemporary methods across various sizes of training samples. The experimental results show that the proposed architecture can be trained with small size datasets and outperforms the state-of-the-art methods in terms of the Overall Accuracy, Average Accuracy, Kappa Value, and training time.<\/jats:p>","DOI":"10.3390\/rs12193137","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T01:39:33Z","timestamp":1600997973000},"page":"3137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A High-Performance Spectral-Spatial Residual Network for Hyperspectral Image Classification with Small Training Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7541-3772","authenticated-orcid":false,"given":"Wijayanti Nurul","family":"Khotimah","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-3257","authenticated-orcid":false,"given":"Mohammed","family":"Bennamoun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7250-7407","authenticated-orcid":false,"given":"Farid","family":"Boussaid","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1557-4907","authenticated-orcid":false,"given":"Ferdous","family":"Sohel","sequence":"additional","affiliation":[{"name":"Information Technology, Mathematics &amp; Statistics, Murdoch University, 90 South Street, Murdoch, Perth, WA 6150, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7599-6760","authenticated-orcid":false,"given":"David","family":"Edwards","sequence":"additional","affiliation":[{"name":"School of Plant Biology and The UWA Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","article-title":"Medical hyperspectral imaging: A review","volume":"19","author":"Lu","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, L., Sun, C., Fu, Y., Kim, M.H., and Huang, H. (2019, January 16\u201320). Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00822"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","article-title":"Hypersectral Imaging for Military and Security Applications: Combining Myriad Processing and Sensing Techniques","volume":"7","author":"Shimoni","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Stuart, M.B., McGonigle, A.J.S., and Willmott, J.R. (2019). Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors, 19.","DOI":"10.3390\/s19143071"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1146\/annurev-food-032818-121155","article-title":"Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications","volume":"10","author":"Ma","year":"2019","journal-title":"Annu. Rev. Food Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1146\/annurev-phyto-080417-050100","article-title":"Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art","volume":"56","author":"Mahlein","year":"2018","journal-title":"Annu. Rev. Phytopathol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.knosys.2010.07.003","article-title":"An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine","volume":"24","author":"Li","year":"2011","journal-title":"Knowl. Based Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6263","DOI":"10.1109\/TGRS.2018.2828601","article-title":"Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral\u2013spatial classification of hyperspectral images","volume":"56","author":"Cao","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.neucom.2015.10.004","article-title":"Iterative deep learning for image set based face and object recognition","volume":"174","author":"Shah","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6440","DOI":"10.1109\/TGRS.2018.2838665","article-title":"Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach","volume":"56","author":"Haut","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative Adversarial Networks for Hyperspectral Image Classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2017.2780890","article-title":"Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks","volume":"15","author":"Zhan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., and Bochtis, D. (2019). Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11060676"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.1109\/TGRS.2018.2815613","article-title":"Hyperspectral image classification with deep learning models","volume":"56","author":"Yang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1109\/LGRS.2019.2900704","article-title":"Unsupervised Multitemporal Domain Adaptation With Source Labels Learning","volume":"16","author":"Liu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework","volume":"56","author":"Zhong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018). A Fast Dense Spectral\u2013Spatial Convolution Network Framework for Hyperspectral Images Classification. Remote Sens., 10.","DOI":"10.3390\/rs10071068"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/JSTARS.2019.2913097","article-title":"Capsulenet-Based Spatial\u2013Spectral Classifier for Hyperspectral Images","volume":"12","author":"PV","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhou, F., Hang, R., and Yuan, X. (2017). Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9121330"},{"key":"ref_23","first-page":"5893","article-title":"Spectral-Spatial Unified Networks for Hyperspectral Image Classification","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/LGRS.2019.2921225","article-title":"Unsupervised Spectral-Spatial Feature Extraction With Generalized Autoencoder for Hyperspectral Imagery","volume":"17","author":"Koda","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2017.2698503","article-title":"Learning and Transferring Deep Joint Spectral-Spatial Features for Hyperspectral Classification","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","unstructured":"Hu, W.S., Li, H.C., Pan, L., Li, W., Tao, R., and Du, Q. (2019). Feature Extraction and Classification Based on Spatial-Spectral ConvLSTM Neural Network for Hyperspectral Images. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2018.02.105","article-title":"Hyperspectral image classification using spectral-spatial LSTMs","volume":"328","author":"Zhou","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_28","first-page":"016005","article-title":"Spectral\u2013spatial classification of hyperspectral image using three-dimensional convolution network","volume":"12","author":"Liu","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1109\/LGRS.2016.2608963","article-title":"Hyperspectral Image Classification Based on Nonlinear Spectral-Spatial Network","volume":"13","author":"Pan","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1109\/TGRS.2017.2778343","article-title":"Two-stream deep architecture for hyperspectral image classification","volume":"56","author":"Hao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"102157","article-title":"Deep Fusion of Localized Spectral Features and Multi-scale Spatial Features for Effective Classification of Hyperspectral Images","volume":"91","author":"Sun","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/LGRS.2019.2945122","article-title":"Combining t-Distributed Stochastic Neighbor Embedding With Convolutional Neural Networks for Hyperspectral Image Classification","volume":"17","author":"Gao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, M., Li, B., and Chen, H. (2017, January 17\u201320). Multi-scale 3D deep convolutional neural network for hyperspectral image classification. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297014"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4420","DOI":"10.1109\/TGRS.2018.2818945","article-title":"3-D deep learning approach for remote sensing image classification","volume":"56","author":"Benoit","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/TGRS.2018.2871782","article-title":"Capsule Networks for Hyperspectral Image Classification","volume":"57","author":"Paoletti","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., and Mei, T. (2017, January 21\u201326). Learning spatio-temporal representation with pseudo-3d residual networks. Proceedings of the IEEE International Conference on Computer Vision, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.590"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4048","DOI":"10.1109\/JSTARS.2018.2874225","article-title":"Active-Learning-Incorporated Deep Transfer Learning for Hyperspectral Image Classification","volume":"11","author":"Lin","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1109\/TGRS.2018.2867679","article-title":"Conditional Random Field and Deep Feature Learning for Hyperspectral Image Classification","volume":"57","author":"Alam","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_40","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (26\u20131, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_42","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.icte.2019.06.001","article-title":"Competitive residual neural network for image classification","volume":"6","author":"Hanif","year":"2020","journal-title":"ICT Express"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Ma, L., Jiang, H., and Zhao, H. (2017, January 23\u201328). Deep residual networks for hyperspectral image classification. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127330"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep pyramidal residual networks for spectral\u2013spatial hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/LGRS.2018.2873476","article-title":"Dual-path network-based hyperspectral image classification","volume":"16","author":"Kang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1109\/JSTARS.2016.2521898","article-title":"Hyperspectral Unmixing in the Presence of Mixed Noise Using Joint-Sparsity and Total Variation","volume":"9","author":"Aggarwal","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 8\u201316). Identity mappings in deep residual networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Khan, S.H., Rahmani, H., Shah, S.A.A., and Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision, Morgan & Claypool.","DOI":"10.1007\/978-3-031-01821-3"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kim, J., Song, J., and Lee, J.K. (2019, January 26\u201328). Recognizing and Classifying Unknown Object in BIM Using 2D CNN. Proceedings of the International Conference on Computer-Aided Architectural Design Futures, Daejeon, Korea.","DOI":"10.1007\/978-981-13-8410-3_4"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Parmar, P., and Morris, B. (2019). HalluciNet-ing Spatiotemporal Representations Using 2D-CNN. arXiv.","DOI":"10.3390\/signals2030037"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1109\/LGRS.2017.2764915","article-title":"Hyperspectral Images Classification with Gabor Filtering and Convolutional Neural Network","volume":"14","author":"Chen","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1109\/TGRS.2018.2860464","article-title":"Feature extraction with multiscale covariance maps for hyperspectral image classification","volume":"57","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TGRS.2018.2865953","article-title":"Hyperspectral Image Classification With Squeeze Multibias Network","volume":"57","author":"Fang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","unstructured":"Han, X.F., Laga, H., and Bennamoun, M. (2019). Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era. IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., and Satoh, Y. (2018, January 18\u201322). Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00685"},{"key":"ref_58","unstructured":"Gonda, F., Wei, D., Parag, T., and Pfister, H. (2018). Parallel separable 3D convolution for video and volumetric data understanding. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., and Paluri, M. (2018, January 18\u201322). A closer look at spatiotemporal convolutions for action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"He, K., and Sun, J. (2015, January 7\u201312). Convolutional neural networks at constrained time cost. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"Hybridsn: Exploring 3-d-2-d cnn feature hierarchy for hyperspectral image classification","volume":"17","author":"Roy","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_62","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France."},{"key":"ref_63","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th international conference on machine learning (ICML-10), Haifa, Israel."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_65","unstructured":"Baumgardner, M.F., Biehl, L.L., and Landgrebe, D.A. (2020, January 20). 220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3. Available online: https:\/\/doi.org\/doi:10.4231\/R7RX991C."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_67","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Singh, S., and Krishnan, S. (2020, January 14\u201319). Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01125"},{"key":"ref_69","unstructured":"Lian, X., and Liu, J. (2019, January 16\u201318). Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization. Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, Okinawa, Japan."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3137\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:13:13Z","timestamp":1760177593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,24]]},"references-count":69,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193137"],"URL":"https:\/\/doi.org\/10.3390\/rs12193137","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,24]]}}}