{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:10:01Z","timestamp":1775326201709,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000},"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>Recently, generative adversarial network (GAN)-based methods for hyperspectral image (HSI) classification have attracted research attention due to their ability to alleviate the challenges brought by having limited labeled samples. However, several studies have demonstrated that existing GAN-based HSI classification methods are limited in redundant spectral knowledge and cannot extract discriminative characteristics, thus affecting classification performance. In addition, GAN-based methods always suffer from the model collapse, which seriously hinders their development. In this study, we proposed a semi-supervised adaptive weighting feature fusion generative adversarial network (AWF2-GAN) to alleviate these problems. We introduced unlabeled data to address the issue of having a small number of samples. First, to build valid spectral\u2013spatial feature engineering, the discriminator learns both the dense global spectrum and neighboring separable spatial context via well-designed extractors. Second, a lightweight adaptive feature weighting component is proposed for feature fusion; it considers four predictive fusion options, that is, adding or concatenating feature maps with similar or adaptive weights. Finally, for the mode collapse, the proposed AWF2-GAN combines supervised central loss and unsupervised mean minimization loss for optimization. Quantitative results on two HSI datasets show that our AWF2-GAN achieves superior performance over state-of-the-art GAN-based methods.<\/jats:p>","DOI":"10.3390\/rs13020198","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T23:03:42Z","timestamp":1610319822000},"page":"198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Adaptive Weighting Feature Fusion Approach Based on Generative Adversarial Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6046-9340","authenticated-orcid":false,"given":"Hongbo","family":"Liang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China"},{"name":"The Key Laboratory of Images &amp; Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0267-5824","authenticated-orcid":false,"given":"Wenxing","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China"},{"name":"The Key Laboratory of Images &amp; Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5501-4528","authenticated-orcid":false,"given":"Xiangfei","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China"},{"name":"The Key Laboratory of Images &amp; Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., D\u2019Urso, G., Mauser, W., Vuolo, F., and Hank, T. (2018). Evaluation of the PROSAIL model capabilities for future hyperspectral model environments: A review study. Remote Sens., 10.","DOI":"10.3390\/rs10010085"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gerhards, M., Schlerf, M., Mallick, K., and Udelhoven, T. (2019). Challenges and future perspectives of multi-\/Hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens., 11.","DOI":"10.3390\/rs11101240"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vali, A., Comai, S., and Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12152495"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chang, C.I., Song, M., Zhang, J., and Wu, C.C. (2019). Editorial for Special Issue \u201cHyperspectral Imaging and Applications\u201d. Remote Sens., 11.","DOI":"10.3390\/rs11172012"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"026009","DOI":"10.1117\/1.JRS.11.026009","article-title":"Assessing the performance of multiple spectral\u2013spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network","volume":"11","author":"Pullanagari","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., and Zhao, C. (2020). A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades. Remote Sens., 12.","DOI":"10.3390\/rs12193188"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kycko, M., Zagajewski, B., Lavender, S., and Dabija, A. (2019). In situ hyperspectral remote sensing for monitoring of alpine trampled and recultivated species. Remote Sens., 11.","DOI":"10.3390\/rs11111296"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ding, C., Li, Y., Xia, Y., Wei, W., Zhang, L., and Zhang, Y. (2017). Convolutional neural networks based hyperspectral image classification method with adaptive kernels. Remote Sens., 9.","DOI":"10.3390\/rs9060618"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TCYB.2018.2810806","article-title":"Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image","volume":"49","author":"Luo","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Meng, Z., Li, L., Jiao, L., Feng, Z., Tang, X., and Liang, M. (2019). Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11222718"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shi, G., Huang, H., Liu, J., Li, Z., and Wang, L. (2019). Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11202414"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"333","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_14","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_15","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_16","doi-asserted-by":"crossref","first-page":"4520","DOI":"10.1109\/TGRS.2017.2693346","article-title":"Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks","volume":"55","author":"Mei","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","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":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","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_19","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_20","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_21","doi-asserted-by":"crossref","unstructured":"Liang, M., Jiao, L., and Meng, Z. (2019). A superpixel-based relational auto-encoder for feature extraction of hyperspectral images. Remote Sens., 11.","DOI":"10.3390\/rs11202454"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, J., He, L., and Wang, Y. (2019). Superpixel-guided layer-wise embedding CNN for remote sensing image classification. Remote Sens., 11.","DOI":"10.3390\/rs11020174"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018). A fast dense spectral\u2013spatial convolution network framework for hyperspectral images classification. Remote Sens., 10.","DOI":"10.3390\/rs10071068"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, K., Chen, Y., Ghamisi, P., Jia, X., and Benediktsson, J.A. (2019). Deep convolutional capsule network for hyperspectral image spectral and spectral-spatial classification. Remote Sens., 11.","DOI":"10.3390\/rs11030223"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cui, X., Zheng, K., Gao, L., Zhang, B., Yang, D., and Ren, J. (2019). Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification. Remote Sens., 11.","DOI":"10.3390\/rs11192220"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6792","DOI":"10.1109\/TGRS.2019.2908679","article-title":"Feature fusion with predictive weighting for spectral image classification and segmentation","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 22\u201325). Feature pyramid networks for object detection. Proceedings of the CVPR 2017\u20142017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fang, S., Quan, D., Wang, S., Zhang, L., and Zhou, L. (2018, January 22\u201327). A Two-Branch Network with Semi-Supervised Learning for Hyperspectral Classification. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517816"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hu, Y., An, R., Wang, B., Xing, F., and Ju, F. (2020). Shape Adaptive Neighborhood Information-Based Semi-Supervised Learning for Hyperspectral Image Classification. Remote Sens., 12.","DOI":"10.3390\/rs12182976"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1109\/TGRS.2019.2949180","article-title":"Multiscale dynamic graph convolutional network for hyperspectral image classification","volume":"58","author":"Wan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, W., Chen, X., Chen, J., and Qu, Y. (2020). Sample Generation with Self-Attention Generative Adversarial Adaptation Network (SaGAAN) for Hyperspectral Image Classification. Remote Sens., 12.","DOI":"10.3390\/rs12050843"},{"key":"ref_34","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_35","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_36","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_37","doi-asserted-by":"crossref","first-page":"3318","DOI":"10.1109\/TCYB.2019.2915094","article-title":"Generative adversarial networks and conditional random fields for hyperspectral image classification","volume":"50","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gao, H., Yao, D., Wang, M., Li, C., Liu, H., Hua, Z., and Wang, J. (2019). A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks. Sensors, 19.","DOI":"10.3390\/s19153269"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Wang, J., Gao, F., Dong, J., and Du, Q. (2020). Adaptive DropBlock-Enhanced Generative Adversarial Networks for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens., 1\u201314.","DOI":"10.1109\/TGRS.2020.2993804"},{"key":"ref_41","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016, January 20). Unsupervised representation learning with deep convolutional generative adversarial networks. Proceedings of the International Conference on Learning Representations ICLR, Toulon, France."},{"key":"ref_42","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_43","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","unstructured":"Odena, A., Olah, C., and Shlens, J. (2017). Conditional Image Synthesis With Auxiliary Classifier GANs. International Conference on Machine Learning, PMLR."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., and Qiao, Y. (2016). A discriminative feature learning approach for deep face recognition. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cai, Y., Dong, Z., Cai, Z., Liu, X., and Wang, G. (2019, January 24\u201326). Discriminative Spectral-Spatial Attention-Aware Residual 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.2019.8921022"},{"key":"ref_47","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 13\u201316). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/LGRS.2012.2203784","article-title":"Automatic generation of standard deviation attribute profiles for spectral\u2013spatial classification of remote sensing data","volume":"10","author":"Marpu","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/198\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:08:35Z","timestamp":1760159315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/198"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,8]]},"references-count":49,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020198"],"URL":"https:\/\/doi.org\/10.3390\/rs13020198","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,8]]}}}