{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:10:59Z","timestamp":1775326259013,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"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":["61877066"],"award-info":[{"award-number":["61877066"]}],"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":["20175181013"],"award-info":[{"award-number":["20175181013"]}],"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":["21RGZN0010"],"award-info":[{"award-number":["21RGZN0010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Aero-Science Fund","award":["61877066"],"award-info":[{"award-number":["61877066"]}]},{"name":"Aero-Science Fund","award":["20175181013"],"award-info":[{"award-number":["20175181013"]}]},{"name":"Aero-Science Fund","award":["21RGZN0010"],"award-info":[{"award-number":["21RGZN0010"]}]},{"name":"Science and technology plan project of Xi\u2019an","award":["61877066"],"award-info":[{"award-number":["61877066"]}]},{"name":"Science and technology plan project of Xi\u2019an","award":["20175181013"],"award-info":[{"award-number":["20175181013"]}]},{"name":"Science and technology plan project of Xi\u2019an","award":["21RGZN0010"],"award-info":[{"award-number":["21RGZN0010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The lack of labeled samples severely restricts the classification performance of deep learning on hyperspectral image classification. To solve this problem, Generative Adversarial Networks (GAN) are usually used for data augmentation. However, GAN have several problems with this task, such as the poor quality of the generated samples and an unstable training process. Thereby, knowing how to construct a GAN to generate high-quality hyperspectral training samples is meaningful for the small-sample classification task of hyperspectral data. In this paper, an Auxiliary Classifier based Wasserstein GAN with Gradient Penalty (AC-WGAN-GP) was proposed. The framework includes AC-WGAN-GP, an online generation mechanism, and a sample selection algorithm. The proposed method has the following distinctive advantages. First, the input of the generator is guided by prior knowledge and a separate classifier is introduced to the architecture of AC-WGAN-GP to produce reliable labels. Second, an online generation mechanism ensures the diversity of generated samples. Third, generated samples that are similar to real data are selected. Experiments on three public hyperspectral datasets show that the generated samples follow the same distribution as the real samples and have enough diversity, which effectively expands the training set. Compared to other competitive methods, the proposed framework achieved better classification accuracy with a small number of labeled samples.<\/jats:p>","DOI":"10.3390\/rs14194910","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["AC-WGAN-GP: Generating Labeled Samples for Improving Hyperspectral Image Classification with Small-Samples"],"prefix":"10.3390","volume":"14","author":[{"given":"Caihao","family":"Sun","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Xiaohua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Hongyun","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Xianghai","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jinhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MGRS.2018.2854840","article-title":"New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning","volume":"6","author":"Ghamisi","year":"2018","journal-title":"IEEE Geo-Sci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral image data analysis","volume":"19","author":"Landgrebe","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MSP.2013.2278992","article-title":"Hyperspectral Target Detection: An Overview of Current and Future Challenges","volume":"31","author":"Nasrabadi","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","first-page":"55","article-title":"Analysis of spectral absorption features in hyperspectral imagery","volume":"5","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hege, E.K., O\u2019Connell, D., Johnson, W., Basty, S., and Dereniak, E.L. (2004). Hyperspectral imaging for astronomy and space surveillance. Imaging Spectrometry IX, SPIE.","DOI":"10.1117\/12.506426"},{"key":"ref_7","unstructured":"Lacar, F.M., Lewis, M.M., and Grierson, I.T. (2001, January 9\u201313). Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia. Proceedings of the IGARSS 2001, Scanning the Present and Resolving the Future, Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, NSW, Australia."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1080\/0143116031000066954","article-title":"Remote sensing of the coastal zone: An overview and priorities for future research","volume":"24","author":"Malthus","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/JSTARS.2010.2095495","article-title":"High Performance Computing for Hyperspectral Remote Sensing","volume":"4","author":"Plaza","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.1109\/TNNLS.2016.2572204","article-title":"A Novel Locally Linear KNN Method With Applications to Visual Recognition","volume":"28","author":"Liu","year":"2016","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10994-005-0463-6","article-title":"Multicategory Proximal Support Vector Machine Classifiers","volume":"59","author":"Fung","year":"2005","journal-title":"Mach. Learn."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7008","DOI":"10.1109\/TGRS.2014.2306687","article-title":"A Novel Spatial\u2013Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyper-spectral Imagery","volume":"52","author":"Pu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Touil, M., Boudebza, I.E., and Daamouche, A. (2015, January 13\u201315). Classification of hyperspectral data using grey model. Proceedings of the 2015 4th International Conference on Electrical Engineering (ICEE), Boumerdes, Algeria.","DOI":"10.1109\/INTEE.2015.7416852"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhang, R., and Wu, Q. (2016, January 21\u201324). Hyperspectral image classification based on PCA network. Proceedings of the 2016 8th Workshop on Hyper-Spectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071787"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cihan, M., and Ceylan, M. (2021, January 9\u201311). Comparison of Linear Discriminant Analysis, Support Vector Machines and Naive Bayes Methods in the Classification of Neonatal Hyperspectral Signatures. Proceedings of the 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey.","DOI":"10.1109\/SIU53274.2021.9477861"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1109\/LGRS.2018.2800034","article-title":"A Saliency-Based Band Selection Approach for Hyperspectral Imagery Inspired by Scale Selection","volume":"15","author":"Su","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","first-page":"343","article-title":"Conditional random field hyperspectral image classification method based on space-spectral fusion","volume":"49","author":"Wei","year":"2020","journal-title":"Chin. J. Surv. Mapp."},{"key":"ref_19","unstructured":"Qian, X. (2014). Research on Hyperspectral Image Classification Combining Spatial Information and Spectral Information, Harbin Engineering University."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"\u00d6zdemir, A.O.B., Gedik, B.E., and \u00c7etin, C.Y.Y. (2014, January 24\u201327). Hyperspectral classification using stacked autoencoders with deep learning. Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077532"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pooja, K., Nidamanuri, R.R., and Mishra, D. (2019, January 24\u201326). Multi-Scale Dilated Residual Convolutional Neural Network for Hyperspectral Image Classification. Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2019.8921284"},{"key":"ref_23","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. Sensors"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Pu, C., Huang, H., and Li, Z. (October, January 26). Spatial-Spectral Combination Convolutional Neural Network for Hyperspectral Image Classification. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324343"},{"key":"ref_26","unstructured":"Ding, Y., Chong, Y., Pan, S., Wang, Y., and Nie, C. (2021). Spatial-Spectral Unified Adaptive Probability Graph Convolutional Networks for Hyperspectral Image Classification. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315."},{"key":"ref_27","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16 x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"Spectral\u2013Spatial Feature Tokenization Transformer for Hyperspectral Image Classi-fication","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hanachi, R., Sellami, A., Farah, I.R., and Mura, M.D. (2021, January 4\u20135). Semi-supervised Classification of Hyperspectral Image through Deep Encoder-Decoder and Graph Neural Networks. Proceedings of the 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Taiz, Yemen.","DOI":"10.1109\/ICOTEN52080.2021.9493562"},{"key":"ref_30","first-page":"1","article-title":"Hyperspectral Image Classification with Contrastive Self-Supervised Learning Under Limited Labeled Samples","volume":"19","author":"Zhao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","first-page":"1","article-title":"Self-Supervised Learning with a Dual-Branch ResNet for Hyperspectral Image Classification","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/LGRS.2020.2989796","article-title":"Hapke Data Augmentation for Deep Learning-Based Hyperspectral Data Analysis with Limited Samples","volume":"18","author":"Qin","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1109\/LGRS.2019.2945848","article-title":"Hyperspectral Image Classification with Data Augmentation and Classifier Fusion","volume":"17","author":"Wang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Networks. arXiv."},{"key":"ref_35","unstructured":"Odena, A., Olah, C., and Shlens, J. (2016). Conditional Image Synthesis With Auxiliary Classifier GANs. arXiv."},{"key":"ref_36","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":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Li, Y., Lyu, X., Frery, A.C., and Ren, P. (2021). Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training. Remote Sens., 13.","DOI":"10.3390\/rs13122378"},{"key":"ref_39","first-page":"1","article-title":"Self-Supervised Divide-and-Conquer Generative Adversarial Network for Classification of Hyperspectral Images","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6053","DOI":"10.1109\/JSTARS.2022.3192127","article-title":"HyperViTGAN: Semisupervised Generative Adversarial Network With Transformer for Hyperspectral Image Classification","volume":"15","author":"He","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liang, H., Bao, W., Lei, B., Zhang, J., and Qu, K. (October, January 26). Adaptive Neighborhood Strategy Based Generative Adversarial Network for Hyperspectral Image Classification. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324088"},{"key":"ref_42","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017, January 4\u20139). Improved Training of Wasserstein GANs. Proceedings of the NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_43","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein GAN. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1023\/A:1026543900054","article-title":"The Earth Mover\u2019s Distance as a Metric for Image Retrieval","volume":"40","author":"Rubner","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref_45","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Xu, Q., Huang, G., Yuan, Y., Guo, C., Sun, Y., Wu, F., and Weinberger, K. (2018). An empirical study on evaluation metrics of generative adversarial networks. arXiv.","DOI":"10.1109\/BigData.2018.8622525"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sun, W., Zhang, L., and Du, B. (2016, January 21\u201324). Feature extraction using near-isometric linear embeddings for hyperspectral imagery classification. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071664"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"71353","DOI":"10.1109\/ACCESS.2020.2986267","article-title":"Small Sample Classification of Hyperspectral Remote Sensing Images Based on Sequential Joint Deeping Learning Model","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_49","first-page":"1","article-title":"Hyperspectral Imagery Classification Based on Contrastive Learning","volume":"60","author":"Hou","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:44:56Z","timestamp":1760143496000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":49,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194910"],"URL":"https:\/\/doi.org\/10.3390\/rs14194910","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,1]]}}}