{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T02:49:37Z","timestamp":1774320577145,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T00:00:00Z","timestamp":1661558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801388"],"award-info":[{"award-number":["41801388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101458"],"award-info":[{"award-number":["42101458"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42130112"],"award-info":[{"award-number":["42130112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Classification with a few labeled samples has always been a longstanding problem in the field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised feature-learning (ES2FL) method is proposed in this paper. The proposed method can automatically learn deep features conducive to classification without any annotation information, significantly reducing the dependence of deep-learning models on massive labeled samples. Firstly, to utilize the spatial\u2013spectral information in HSIs more fully and effectively, EfficientNet-B0 is introduced and used as the backbone to model input samples. Then, through constraining the cross-correlation matrix of different distortions of the same sample to the identity matrix, the designed model can extract the latent features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. In addition, two ensemble learning strategies, feature-level and view-level ensemble, are proposed to further improve the feature-learning ability and classification performance by jointly utilizing spatial contextual information at different scales and feature information at different bands. Finally, the concatenations of the learned features and the original spectral vectors are inputted into classifiers such as random forest or support vector machine to complete label prediction. Extensive experiments on three widely used HSI data sets show that the proposed ES2FL method can learn more discriminant deep features and achieve better classification performance than existing advanced methods in the case of small samples.<\/jats:p>","DOI":"10.3390\/rs14174236","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4236","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["ES2FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0848-8453","authenticated-orcid":false,"given":"Bing","family":"Liu","sequence":"first","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2145-199X","authenticated-orcid":false,"given":"Kuiliang","family":"Gao","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3332-9668","authenticated-orcid":false,"given":"Anzhu","family":"Yu","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0653-8373","authenticated-orcid":false,"given":"Lei","family":"Ding","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7109-5559","authenticated-orcid":false,"given":"Chunping","family":"Qiu","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MGRS.2018.2867592","article-title":"Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications","volume":"6","author":"Zhong","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5515313","DOI":"10.1109\/TGRS.2021.3101848","article-title":"A Dual-UNet with Multistage Details Injection for Hyperspectral Image Fusion","volume":"60","author":"Xiao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/TGRS.2017.2769673","article-title":"Supervised Deep Feature Extraction for Hyperspectral Image Classification","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10794","DOI":"10.1109\/JSTARS.2021.3121334","article-title":"Patch-Free Bilateral Network for Hyperspectral Image Classification Using Limited Samples","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7307","DOI":"10.1109\/TGRS.2019.2912330","article-title":"Hyperspectral Image Classification with Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement","volume":"57","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/TGRS.2011.2162589","article-title":"An Adaptive Artificial Immune Network for Supervised Classification of Multi-\/Hyperspectral Remote Sensing Imagery","volume":"50","author":"Zhong","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","article-title":"Modern Trends in Hyperspectral Image Analysis: A Review","volume":"6","author":"Khan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TGRS.2017.2743102","article-title":"PCA-Based Edge-Preserving Features for Hyperspectral Image Classification","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/JSTARS.2015.2472460","article-title":"Spatial Regularized Local Manifold Learning for Classification of Hyperspectral Images","volume":"9","author":"Ma","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.1109\/TCYB.2020.2977461","article-title":"Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image","volume":"51","author":"Duan","year":"2021","journal-title":"IEEE T Cybernetics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6547","DOI":"10.1109\/TGRS.2017.2729882","article-title":"Multiple Kernel Learning for Hyperspectral Image Classification: A Review","volume":"55","author":"Gu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"5524617","article-title":"Unsupervised Spatial-Spectral CNN-Based Feature Learning for Hyperspectral Image Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.1109\/JSTARS.2020.3002787","article-title":"Deep Induction Network for Small Samples Classification of Hyperspectral Images","volume":"13","author":"Gao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in Spectral-Spatial Classification of Hyperspectral Images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/JSTARS.2013.2295313","article-title":"Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification","volume":"7","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/TGRS.2017.2754511","article-title":"Local Binary Pattern-Based Hyperspectral Image Classification With Superpixel Guidance","volume":"56","author":"Jia","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1109\/TCYB.2017.2682846","article-title":"A 3-D Gabor Phase-Based Coding and Matching Framework for Hyperspectral Imagery Classification","volume":"48","author":"Jia","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_20","first-page":"5514116","article-title":"Multiscale Deep Learning Network with Self-Calibrated Convolution for Hyperspectral and LiDAR Data Collaborative Classification","volume":"60","author":"Xue","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, H., Lin, Y., Xu, X., Chen, Z., Wu, Z., and Tang, Y. (2022). A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking. Agronomy, 12.","DOI":"10.3390\/agronomy12071520"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107079","DOI":"10.1016\/j.compag.2022.107079","article-title":"Rachis detection and three-dimensional localization of cut off point for vision-based banana robot","volume":"198","author":"Wu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cui, Q., Yang, B., Liu, B., Li, Y., and Ning, J. (2022). Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning. Agriculture, 12.","DOI":"10.3390\/agriculture12081085"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112790","DOI":"10.1016\/j.rse.2021.112790","article-title":"Accurate hyperspectral imaging of mineralised outcrops: An example from lithium-bearing pegmatites at Uis, Namibia","volume":"269","author":"Booysen","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111273","DOI":"10.1016\/j.rse.2019.111273","article-title":"Grassland ecosystem services in a changing environment: The potential of hyperspectral monitoring","volume":"232","author":"Obermeier","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going Deeper with Contextual CNN for Hyperspectral Image Classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1109\/JSTARS.2020.3046414","article-title":"Study of Spatial-Spectral Feature Extraction Frameworks with 3-D Convolutional Neural Network for Robust Hyperspectral Imagery Classification","volume":"14","author":"Praveen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/JSTARS.2020.2983224","article-title":"A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial\u2013Spectral Fusion","volume":"13","author":"Yu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5509612","DOI":"10.1109\/TGRS.2021.3102034","article-title":"Hyperspectral Image Classification Using Attention-Based Bidirectional Long Short-Term Memory Network","volume":"60","author":"Mei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded Recurrent Neural Networks for Hyperspectral Image Classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gao, K., Liu, B., Yu, X., Qin, J., Zhang, P., and Tan, X. (2020). Deep Relation Network for Hyperspectral Image Few-Shot Classification. Remote Sens., 12.","DOI":"10.3390\/rs12060923"},{"key":"ref_33","first-page":"5501916","article-title":"Feedback Attention-Based Dense CNN for Hyperspectral Image Classification","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TGRS.2020.2994057","article-title":"Residual Spectral-Spatial Attention Network for Hyperspectral Image Classification","volume":"59","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"5518714","article-title":"Cross-Attention Spectral Spatial Network for Hyperspectral Image Classification","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3449","DOI":"10.1109\/TIP.2022.3169689","article-title":"Unsupervised Meta Learning with Multiview Constraints for Hyperspectral Image Small Sample set Classification","volume":"31","author":"Gao","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"38509","DOI":"10.1117\/1.JRS.15.038509","article-title":"Deep global-local transformer network combined with extended morphological profiles for hyperspectral image classification","volume":"15","author":"Tan","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TGRS.2015.2478379","article-title":"Unsupervised Deep Feature Extraction for Remote Sensing Image Classification","volume":"54","author":"Romero","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5502315","DOI":"10.1109\/TGRS.2021.3054037","article-title":"Boosting Hyperspectral Image Classification with Unsupervised Feature Learning","volume":"60","author":"Wei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6808","DOI":"10.1109\/TGRS.2019.2908756","article-title":"Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification","volume":"57","author":"Mei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/TMM.2019.2928491","article-title":"Multiscale Superpixel-Based Hyperspectral Image Classification Using Recurrent Neural Networks With Stacked Autoencoders","volume":"22","author":"Shi","year":"2020","journal-title":"IEEE Trans. Multimedia"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.1109\/JSTARS.2018.2854893","article-title":"Marginal Stacked Autoencoder with Adaptively-Spatial Regularization for Hyperspectral Image Classification","volume":"11","author":"Feng","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2017.2748160","article-title":"Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classification","volume":"56","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.1109\/TGRS.2018.2876123","article-title":"Unsupervised Feature Extraction in Hyperspectral Images Based on Wasserstein Generative Adversarial Network","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","first-page":"5506010","article-title":"Convolutional Two-Stream Generative Adversarial Network-Based Hyperspectral Feature Extraction","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","article-title":"Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey","volume":"43","author":"Jing","year":"2021","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, F., Hou, Z., Wang, Z., Mian, L., Zhang, J., and Tang, J. (2020). Self-supervised Learning: Generative or Contrastive. IEEE Trans. Knowl. Data Eng.","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"ref_49","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S. (2021, January 18\u201324). Barlow Twins: Self-Supervised Learning via Redundancy Reduction. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_50","unstructured":"Tan, M., and Le, Q.V. (2019, January 13). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the Machine Learning Research, Vancouver, BC, Canada."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7758","DOI":"10.1109\/TGRS.2020.3034133","article-title":"Deep Multiview Learning for Hyperspectral Image Classification","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (2017). Searching for Activation Functions. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral Image Classification Using Deep Pixel-Pair Features","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/2150704X.2018.1526424","article-title":"A dense convolutional neural network for hyperspectral image classification","volume":"10","author":"Zhi","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_55","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_57","unstructured":"Joachims, T. (1999, January 27\u201330). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the Sixteenth International Conference on Machine Learning, Bled, Slovenia."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"26516","DOI":"10.1117\/1.JRS.14.026516","article-title":"Semisupervised graph convolutional network for hyperspectral image classification","volume":"14","author":"Liu","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5500615","DOI":"10.1109\/TGRS.2021.3052048","article-title":"Generative Adversarial Minority Oversampling for Spectral\u2013Spatial Hyperspectral Image Classification","volume":"60","author":"Roy","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TGRS.2018.2872830","article-title":"Deep Few-Shot Learning for Hyperspectral Image Classification","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xu, Q., Xiao, Y., Wang, D., and Luo, B. (2020). CSA-MSO3DCNN: Multiscale Octave 3D CNN with Channel and Spatial Attention for Hyperspectral Image Classification. Remote Sens., 12.","DOI":"10.3390\/rs12010188"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4789","DOI":"10.1109\/JSTARS.2020.3016739","article-title":"Deep Collaborative Attention Network for Hyperspectral Image Classification by Combining 2-D CNN and 3-D CNN","volume":"13","author":"Guo","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ma, W., Yang, Q., Wu, Y., Zhao, W., and Zhang, X. (2019). Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11111307"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1080\/01431161.2022.2054299","article-title":"Resolution reconstruction classification: Fully octave convolution network with pyramid attention mechanism for hyperspectral image classification","volume":"43","author":"Sun","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","first-page":"2579","article-title":"Viualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4236\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:18:57Z","timestamp":1760141937000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,27]]},"references-count":65,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174236"],"URL":"https:\/\/doi.org\/10.3390\/rs14174236","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,27]]}}}