{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T14:25:54Z","timestamp":1775831154488,"version":"3.50.1"},"reference-count":93,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T00:00:00Z","timestamp":1690675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In deep learning-based hyperspectral remote sensing image classification tasks, random sampling strategies are typically used to train model parameters for testing and evaluation. However, this approach leads to strong spatial autocorrelation between the training set samples and the surrounding test set samples, and some unlabeled test set data directly participate in the training of the network. This leaked information makes the model overly optimistic. Models trained under these conditions tend to overfit to a single dataset, which limits the range of practical applications. This paper analyzes the causes and effects of information leakage and summarizes the methods from existing models to mitigate the effects of information leakage. Specifically, this paper states the main issues in this area, where the issue of information leakage is addressed in detail. Second, some algorithms and related models used to mitigate information leakage are categorized, including reducing the number of training samples, using spatially disjoint sampling strategies, few-shot learning, and unsupervised learning. These models and methods are classified according to the sample-related phase and the feature extraction phase. Finally, several representative hyperspectral image classification models experiments are conducted on the common datasets and their effectiveness in mitigating information leakage is analyzed.<\/jats:p>","DOI":"10.3390\/rs15153793","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T01:48:50Z","timestamp":1690768130000},"page":"3793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Information Leakage in Deep Learning-Based Hyperspectral Image Classification: A Survey"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7948-4090","authenticated-orcid":false,"given":"Hao","family":"Feng","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1647-2956","authenticated-orcid":false,"given":"Yongcheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3078-1886","authenticated-orcid":false,"given":"Zheng","family":"Li","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1920-0649","authenticated-orcid":false,"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}]},{"given":"Yuxi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yunxiao","family":"Gao","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5205","DOI":"10.1007\/s10462-021-10018-y","article-title":"A review of deep learning used in the hyperspectral image analysis for agriculture","volume":"54","author":"Wang","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","first-page":"316","article-title":"An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation","volume":"6","author":"Awad","year":"2019","journal-title":"Inf. Process. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/B978-0-444-63977-6.00018-3","article-title":"Hyperspectral imaging in crop fields: Precision agriculture","volume":"Volume 32","author":"Caballero","year":"2019","journal-title":"Data Handling in Science and Technology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, B., Liu, Z., Men, S., Li, Y., Ding, Z., He, J., and Zhao, Z. (2020). Underwater hyperspectral imaging technology and its applications for detecting and mapping the seafloor: A review. Sensors, 20.","DOI":"10.3390\/s20174962"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.rse.2014.01.026","article-title":"A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data","volume":"147","author":"Jay","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gross, W., Queck, F., V\u00f6gtli, M., Schreiner, S., Kuester, J., B\u00f6hler, J., Mispelhorn, J., Kneub\u00fchler, M., and Middelmann, W. (2021, January 13\u201317). A multi-temporal hyperspectral target detection experiment: Evaluation of military setups. Proceedings of the Target and Background Signatures VII. SPIE, Online.","DOI":"10.1117\/12.2597991"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Contreras Acosta, I.C., Khodadadzadeh, M., and Gloaguen, R. (2021). Resolution enhancement for drill-core hyperspectral mineral mapping. Remote Sens., 13.","DOI":"10.3390\/rs13122296"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"79534","DOI":"10.1109\/ACCESS.2021.3068392","article-title":"Trends in deep learning for medical hyperspectral image analysis","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"106456","DOI":"10.1016\/j.compeleceng.2019.106456","article-title":"An innovative multi-kernel learning algorithm for hyperspectral classification","volume":"79","author":"Li","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, G., Wang, L., Liu, D., Fei, L., and Yang, J. (2022). Hyperspectral Image Classification Based on Non-Parallel Support Vector Machine. Remote Sens., 14.","DOI":"10.3390\/rs14102447"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/LGRS.2017.2648515","article-title":"Spatial logistic regression for support-vector classification of hyperspectral imagery","volume":"14","author":"Liu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","first-page":"1","article-title":"Kronecker factorization-based multinomial logistic regression for hyperspectral image classification","volume":"19","author":"Wang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1109\/LGRS.2014.2320258","article-title":"A subspace-based multinomial logistic regression for hyperspectral image classification","volume":"11","author":"Khodadadzadeh","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, H., Gao, L., Li, J., Li, S.S., Zhang, B., and Benediktsson, J.A. (2016). Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields. Remote Sens., 8.","DOI":"10.3390\/rs8040355"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Samat, A., Gamba, P., Abuduwaili, J., Liu, S., and Miao, Z. (2016). Geodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transfer. Remote Sens., 8.","DOI":"10.3390\/rs8030234"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8492","DOI":"10.1109\/JSTARS.2022.3209349","article-title":"Orientation-First Strategy With Angle Attention Module for Rotated Object Detection in Remote Sensing Images","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., Ben, G., and Gao, Y. (2022). Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey. Remote Sens., 14.","DOI":"10.3390\/rs14102385"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, B., He, B., Wu, L., and Guo, Z. (2021). Deep residual dual-attention network for super-resolution reconstruction of remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13142784"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/MGRS.2021.3063465","article-title":"Change detection from very-high-spatial-resolution optical remote sensing images: Methods, applications, and future directions","volume":"9","author":"Wen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/LGRS.2018.2830403","article-title":"Deformable convolutional neural networks for hyperspectral image classification","volume":"15","author":"Zhu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2015.1047045","article-title":"Spectral\u2013spatial classification of hyperspectral images using deep convolutional neural networks","volume":"6","author":"Yue","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"7831","DOI":"10.1109\/TGRS.2020.3043267","article-title":"Attention-based adaptive spectral\u2013spatial kernel ResNet for hyperspectral image classification","volume":"59","author":"Roy","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3085522","article-title":"SPN: Stable prototypical network for few-shot learning-based hyperspectral image classification","volume":"19","author":"Pal","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","first-page":"1","article-title":"Few-shot hyperspectral image classification based on adaptive subspaces and feature transformation","volume":"60","author":"Bai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2021.03.035","article-title":"A survey: Deep learning for hyperspectral image classification with few labeled samples","volume":"448","author":"Jia","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Molinier, M., and Kilpi, J. (August, January 28). Avoiding overfitting when applying spectral-spatial deep learning methods on hyperspectral images with limited labels. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900328"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/LGRS.2019.2895697","article-title":"Validating hyperspectral image segmentation","volume":"16","author":"Nalepa","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TGRS.2016.2616489","article-title":"On the sampling strategy for evaluation of spectral-spatial methods in hyperspectral image classification","volume":"55","author":"Liang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, J., Liang, J., Qian, Y., Gao, Y., and Tong, L. (2015, January 2\u20135). On the sampling strategies for evaluation of joint spectral-spatial information based classifiers. Proceedings of the 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan.","DOI":"10.1109\/WHISPERS.2015.8075474"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qu, L., Zhu, X., Zheng, J., and Zou, L. (2021). Triple-attention-based parallel network for hyperspectral image classification. Remote Sens., 13.","DOI":"10.3390\/rs13020324"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep learning for classification of hyperspectral data: A comparative review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"6808","DOI":"10.1109\/TGRS.2019.2908756","article-title":"Unsupervised spatial\u2013spectral feature learning by 3D convolutional autoencoder for hyperspectral classification","volume":"57","author":"Mei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2017.2748160","article-title":"Unsupervised spectral\u2013spatial feature learning via deep residual Conv\u2013Deconv network for hyperspectral image classification","volume":"56","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"104317","DOI":"10.1016\/j.infrared.2022.104317","article-title":"Band selection for heterogeneity classification of hyperspectral transmission images based on multi-criteria ranking","volume":"125","author":"Li","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5580","DOI":"10.1007\/s11356-022-24202-2","article-title":"Dimensionality reduction strategies for land use land cover classification based on airborne hyperspectral imagery: A survey","volume":"30","author":"Moharram","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1109\/LGRS.2020.3007433","article-title":"RMCNet: Random Multiscale Convolutional Network for Hyperspectral Image Classification","volume":"18","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, T., Shi, C., Liao, D., and Wang, L. (2021). Deep spectral spatial inverted residual network for hyperspectral image classification. Remote Sens., 13.","DOI":"10.3390\/rs13214472"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/MGRS.2022.3169947","article-title":"Active learning for hyperspectral image classification: A comparative review","volume":"10","author":"Thoreau","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1080\/014311600210434","article-title":"A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data","volume":"21","author":"Friedl","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1080\/01431169008955066","article-title":"Limitations to the identification of spatial structures from AVHRR data","volume":"11","author":"Belward","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6914","DOI":"10.1080\/01431161.2013.810822","article-title":"Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification","volume":"34","author":"Zhen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep learning classifiers for hyperspectral imaging: A review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/JSTARS.2020.2968179","article-title":"Spectral\u2013Spatial exploration for hyperspectral image classification via the fusion of fully convolutional networks","volume":"13","author":"Zou","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","first-page":"1","article-title":"S3Net: Spectral\u2013spatial Siamese network for few-shot hyperspectral image classification","volume":"60","author":"Xue","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TGRS.2017.2765364","article-title":"Recent advances on spectral\u2013spatial hyperspectral image classification: An overview and new guidelines","volume":"56","author":"He","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"Perceiving Spectral Variation: Unsupervised Spectrum Motion Feature Learning for Hyperspectral Image Classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","first-page":"1","article-title":"Nonoverlapped Sampling for Hyperspectral Imagery: Performance Evaluation and a Cotraining-Based Classification Strategy","volume":"60","author":"Cao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1080\/2150704X.2017.1280200","article-title":"Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1109\/TIP.2018.2809606","article-title":"Diverse region-based CNN for hyperspectral image classification","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/LGRS.2018.2878773","article-title":"Data augmentation for hyperspectral image classification with deep CNN","volume":"16","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8063","DOI":"10.1109\/JSTARS.2021.3102610","article-title":"Dynamic data augmentation method for hyperspectral image classification based on Siamese structure","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"10473","DOI":"10.1109\/TGRS.2020.3046840","article-title":"Spectral\u2013spatial fractal residual convolutional neural network with data balance augmentation for hyperspectral classification","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","first-page":"1","article-title":"Iterative spatial-spectral training sample augmentation for effective hyperspectral image classification","volume":"19","author":"Shang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative adversarial networks: An overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Neagoe, V.E., and Diaconescu, P. (2020, January 18\u201320). CNN hyperspectral image classification using training sample augmentation with generative adversarial networks. Proceedings of the 2020 13th International Conference on Communications (COMM), Bucharest, Romania.","DOI":"10.1109\/COMM48946.2020.9142021"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2020.3041864","article-title":"Mixture of spectral generative adversarial networks for imbalanced hyperspectral image classification","volume":"19","author":"Dam","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.1109\/JSTARS.2021.3115971","article-title":"Spectral\u2013spatial attention feature extraction for hyperspectral image classification based on generative adversarial network","volume":"14","author":"Liang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1109\/JSTARS.2020.2992310","article-title":"Delving into classifying hyperspectral images via graphical adversarial learning","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"H\u00e4nsch, R., Ley, A., and Hellwich, O. (2017, January 23\u201328). Correct and still wrong: The relationship between sampling strategies and the estimation of the generalization error. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127795"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lange, J., Cavallaro, G., G\u00f6tz, M., Erlingsson, E., and Riedel, M. (2018, January 22\u201327). The influence of sampling methods on pixel-wise hyperspectral image classification with 3D convolutional neural networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spai.","DOI":"10.1109\/IGARSS.2018.8518671"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, H., Zhang, A., and Liu, Y. (2022). Semantic Segmentation of Hyperspectral Remote Sensing Images Based on PSE-UNet Model. Sensors, 22.","DOI":"10.3390\/s22249678"},{"key":"ref_68","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_69","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, T., Zhou, T., Liu, Y., and Peng, Y. (2021). Contrastive learning based on transformer for hyperspectral image classification. Appl. Sci., 11.","DOI":"10.3390\/app11188670"},{"key":"ref_71","first-page":"1","article-title":"ROBYOL: Random-Occlusion-Based BYOL for Hyperspectral Image Classification","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/TGRS.2020.2997863","article-title":"Class-wise distribution adaptation for unsupervised classification of hyperspectral remote sensing images","volume":"59","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","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_74","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1109\/LGRS.2019.2950441","article-title":"Multiscale CNNs ensemble based self-learning for hyperspectral image classification","volume":"17","author":"Fang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_75","first-page":"1","article-title":"Deep Mutual-Teaching for Hyperspectral Imagery Classification","volume":"19","author":"Zhao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph convolutional networks for hyperspectral image classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/LGRS.2020.2966239","article-title":"Semisupervised classification for hyperspectral images using graph attention networks","volume":"18","author":"Sha","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"He, Z., Xia, K., Li, T., Zu, B., Yin, Z., and Zhang, J. (2021). A constrained graph-based semi-supervised algorithm combined with particle cooperation and competition for hyperspectral image classification. Remote Sens., 13.","DOI":"10.3390\/rs13020193"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Xi, B., Li, J., Li, Y., and Du, Q. (2021, January 11\u201316). Semi-supervised graph prototypical networks for hyperspectral image classification. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553372"},{"key":"ref_80","first-page":"1","article-title":"Hypergraph-structured autoencoder for unsupervised and semisupervised classification of hyperspectral image","volume":"19","author":"Cai","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_81","first-page":"1","article-title":"A Multiscale Spatial\u2013Spectral Prototypical Network for Hyperspectral Image Few-Shot Classification","volume":"19","author":"Tang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"Hyperspectral Image Few-Shot Classification Network Based on the Earth Mover\u2019s Distance","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_83","first-page":"1","article-title":"Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_84","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_85","unstructured":"(2022, July 13). 2013 IEEE GRSS Image Analysis and Data Fusion Contest. Available online: http:\/\/www.grss-ieee.org\/community\/technical-committees\/data-fusion\/."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/JSTARS.2014.2305441","article-title":"Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest","volume":"7","author":"Debes","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"258619","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_89","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TGRS.2020.2994057","article-title":"Residual spectral\u2013Spatial attention network for hyperspectral image classification","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_90","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_91","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":"Hamida","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"9266","DOI":"10.1109\/JSTARS.2022.3216335","article-title":"CSiT: A Multiscale Vision Transformer for Hyperspectral Image Classification","volume":"60","author":"He","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3038405","article-title":"A multi-degradation aided method for unsupervised remote sensing image super resolution with convolution neural networks","volume":"60","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3793\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:22:34Z","timestamp":1760127754000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,30]]},"references-count":93,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153793"],"URL":"https:\/\/doi.org\/10.3390\/rs15153793","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,30]]}}}