{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T01:32:29Z","timestamp":1782523949458,"version":"3.54.5"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China under Grant","award":["62071491"],"award-info":[{"award-number":["62071491"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1906217"],"award-info":[{"award-number":["U1906217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["19CX05003A-11"],"award-info":[{"award-number":["19CX05003A-11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been widely used in hyperspectral image classification (HSIC) tasks. However, the generated HSI virtual samples by VAEs are often ambiguous, and GANs are prone to the mode collapse, which lead the poor generalization abilities ultimately. Moreover, most of these models only consider the extraction of spectral or spatial features. They fail to combine the two branches interactively and ignore the correlation between them. Consequently, the variational generative adversarial network with crossed spatial and spectral interactions (CSSVGAN) was proposed in this paper, which includes a dual-branch variational Encoder to map spectral and spatial information to different latent spaces, a crossed interactive Generator to improve the quality of generated virtual samples, and a Discriminator stuck with a classifier to enhance the classification performance. Combining these three subnetworks, the proposed CSSVGAN achieves excellent classification by ensuring the diversity and interacting spectral and spatial features in a crossed manner. The superior experimental results on three datasets verify the effectiveness of this method.<\/jats:p>","DOI":"10.3390\/rs13163131","type":"journal-article","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T21:35:40Z","timestamp":1628458540000},"page":"3131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Variational Generative Adversarial Network with Crossed Spatial and Spectral Interactions for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhongwei","family":"Li","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1714-4161","authenticated-orcid":false,"given":"Xue","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Xin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangming","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingshuai","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leiquan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"030501","DOI":"10.1117\/1.JRS.10.030501","article-title":"Dimensionality reduction for hyperspectral image classification based on multiview graphs ensemble","volume":"10","author":"Chen","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shi, G., Luo, F., Tang, Y., and Li, Y. (2021). Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding. Remote Sens., 13.","DOI":"10.3390\/rs13071363"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Liu, Q., Hang, R., and Liu, G. (2017). Hypergraph embedding for spatial-spectral joint feature extraction in hyperspectral images. Remote Sens., 9.","DOI":"10.3390\/rs9050506"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Abbate, G., Fiumi, L., De Lorenzo, C., and Vintila, R. (2003, January 22\u201323). Evaluation of remote sensing data for urban planning. Applicative examples by means of multispectral and hyperspectral data. Proceedings of the 2003 2nd GRSS\/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Berlin, Germany.","DOI":"10.1109\/DFUA.2003.1219987"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1179\/174313110X12771950995716","article-title":"An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition","volume":"58","author":"Yuen","year":"2010","journal-title":"Imaging Sci. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4647","DOI":"10.1109\/JSTARS.2015.2453411","article-title":"GPU parallel implementation of support vector machines for hyperspectral image classification","volume":"8","author":"Tan","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","first-page":"318","article-title":"Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression","volume":"10","author":"Li","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.isprsjprs.2015.03.006","article-title":"A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination","volume":"105","author":"Tan","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gao, Q., Lim, S., and Jia, X. (2018). Hyperspectral image classification using convolutional neural networks and multiple feature learning. Remote Sens., 10.","DOI":"10.3390\/rs10020299"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"111938","DOI":"10.1016\/j.rse.2020.111938","article-title":"Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images","volume":"247","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, Y.C., Lei, T.C., Yao, S., and Wang, H.P. (2020). PM2. 5 Prediction Model Based on Combinational Hammerstein Recurrent Neural Networks. Mathematics, 8.","DOI":"10.3390\/math8122178"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nezami, S., Khoramshahi, E., Nevalainen, O., P\u00f6l\u00f6nen, I., and Honkavaara, E. (2020). Tree species classification of drone hyperspectral and rgb imagery with deep learning convolutional neural networks. Remote Sens., 12.","DOI":"10.20944\/preprints202002.0334.v1"},{"key":"ref_15","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":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","first-page":"5893","article-title":"Spectral\u2013spatial unified networks for hyperspectral image classification","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, G., Gao, L., and Qi, L. (2021). Hyperspectral Image Classification via Multieatureased Correlation Adaptive Representation. Remote Sens., 13.","DOI":"10.3390\/rs13071253"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN feature hierarchy for hyperspectral image classification","volume":"17","author":"Roy","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Belwalkar, A., Nath, A., and Dikshit, O. (2018, January 20\u201323). Spectral-Spatial Classification of Hyperspectral Remote Sensing Images Using Variational Autoencoder and Convolution Neural Network. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Dehradun, India.","DOI":"10.5194\/isprs-archives-XLII-5-613-2018"},{"key":"ref_21","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1109\/ACCESS.2020.3047074","article-title":"Multispectral image reconstruction from color images using enhanced variational autoencoder and generative adversarial network","volume":"9","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4309","DOI":"10.1109\/TGRS.2018.2890633","article-title":"DAEN: Deep autoencoder networks for hyperspectral unmixing","volume":"57","author":"Su","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015). Adversarial autoencoders. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bao, J., Chen, D., Wen, F., Li, H., and Hua, G. (2017, January 22\u201329). CVAE-GAN: Fine-grained image generation through asymmetric training. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.299"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, Z., Liu, H., Wang, Y., and Hu, J. (2017). Generative adversarial networks-based semi-supervised learning for hyperspectral image classification. Remote Sens., 9.","DOI":"10.3390\/rs9101042"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_28","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2016, January 16\u201321). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Proceedings of the 30th International Conference on Neural Information Processing Systems, Kyoto, Japan."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Feng, J., Feng, X., Chen, J., Cao, X., Zhang, X., Jiao, L., and Yu, T. (2020). Generative adversarial networks based on collaborative learning and attention mechanism for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12071149"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"5329","DOI":"10.1109\/TGRS.2019.2899057","article-title":"Classification of hyperspectral images based on multiclass spatial\u2013spectral generative adversarial networks","volume":"57","author":"Feng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"5676","DOI":"10.1109\/TGRS.2020.2968304","article-title":"CVA2E: A conditional variational autoencoder with an adversarial training process for hyperspectral imagery classification","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, H., Tao, C., Qi, J., Li, H., and Tang, Y. (August, January 28). Semi-supervised variational generative adversarial networks for hyperspectral image classification. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900073"},{"key":"ref_35","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On information and sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.knosys.2018.11.018","article-title":"Semi-supervised dimensional sentiment analysis with variational autoencoder","volume":"165","author":"Wu","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_38","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein GAN. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., and Paul Smolley, S. (2017, January 22\u201329). Least squares generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_41","first-page":"2234","article-title":"Improved techniques for training gans","volume":"29","author":"Salimans","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.inffus.2020.01.007","article-title":"An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges","volume":"59","author":"Imani","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1109\/TGRS.2019.2951160","article-title":"Spectral\u2013spatial attention network for hyperspectral image classification","volume":"58","author":"Sun","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","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_46","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_47","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_48","unstructured":"Loshchilov, I., and Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:42:11Z","timestamp":1760164931000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,7]]},"references-count":48,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163131"],"URL":"https:\/\/doi.org\/10.3390\/rs13163131","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,7]]}}}