{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:55:58Z","timestamp":1760147758793,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,26]],"date-time":"2023-02-26T00:00:00Z","timestamp":1677369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61801353","61977052","61971273","202107020822","202202022633","2018M633474"],"award-info":[{"award-number":["61801353","61977052","61971273","202107020822","202202022633","2018M633474"]}]},{"name":"GHfund B","award":["61801353","61977052","61971273","202107020822","202202022633","2018M633474"],"award-info":[{"award-number":["61801353","61977052","61971273","202107020822","202202022633","2018M633474"]}]},{"name":"China Postdoctoral Science Foundation","award":["61801353","61977052","61971273","202107020822","202202022633","2018M633474"],"award-info":[{"award-number":["61801353","61977052","61971273","202107020822","202202022633","2018M633474"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The realization of efficient classification with limited labeled samples is a critical task in hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have achieved remarkable advances while considering spectral\u2013spatial features simultaneously, while conventional patch-wise-based CNNs usually lead to redundant computations. Therefore, in this paper, we established a novel active inference transfer convolutional fusion network (AI-TFNet) for HSI classification. First, in order to reveal and merge the local low-level and global high-level spectral\u2013spatial contextual features at different stages of extraction, an end-to-end fully hybrid multi-stage transfer fusion network (TFNet) was designed to improve classification performance and efficiency. Meanwhile, an active inference (AI) pseudo-label propagation algorithm for spatially homogeneous samples was constructed using the homogeneous pre-segmentation of the proposed TFNet. In addition, a confidence-augmented pseudo-label loss (CapLoss) was proposed in order to define the confidence of a pseudo-label with an adaptive threshold in homogeneous regions for acquiring pseudo-label samples; this can adaptively infer a pseudo-label by actively augmenting the homogeneous training samples based on their spatial homogeneity and spectral continuity. Experiments on three real HSI datasets proved that the proposed method had competitive performance and efficiency compared to several related state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs15051292","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T01:59:10Z","timestamp":1677463150000},"page":"1292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["AI-TFNet: Active Inference Transfer Convolutional Fusion Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6704-1198","authenticated-orcid":false,"given":"Jianing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"},{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Linhao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Yichen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Jinyu","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Xiao","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Telecommunications Engineering, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0610-0005","authenticated-orcid":false,"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,26]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TGRS.2014.2358934","article-title":"A survey on spectral\u2013spatial classification techniques based on attribute profiles","volume":"53","author":"Ghamisi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Uzkent, B., Rangnekar, A., and Hoffman, M. (2017, January 21\u201326). Aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","key":"ref_3","DOI":"10.1109\/CVPRW.2017.35"},{"key":"ref_4","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."},{"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 IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217), Sydney, Australia.","key":"ref_5"},{"key":"ref_6","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_7","first-page":"349","article-title":"Subspace-based support vector machines for hyperspectral image classification","volume":"12","author":"Gao","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.1109\/JSTARS.2018.2873051","article-title":"Semisupervised hyperspectral image classification via Laplacian least squares support vector machine in sum space and random sampling","volume":"8","author":"Liu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4063","DOI":"10.1109\/JSTARS.2018.2869376","article-title":"Hyperspectral image classification via weighted joint nearest neighbor and sparse representation","volume":"11","author":"Tu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2008.916090","article-title":"Nearest neighbor classification of remote sensing images with the maximal margin principle","volume":"46","author":"Blanzieri","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/JSTARS.2018.2809781","article-title":"Cascaded random forest for hyperspectral image classification","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.1109\/JSTARS.2015.2396577","article-title":"Random forests unsupervised classification: The detection and mapping of solanum mauritianum infestations in plantation forestry using hyperspectral data","volume":"8","author":"Peerbhay","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/LGRS.2005.857031","article-title":"Composite kernels for hyperspectral image classification","volume":"3","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"doi-asserted-by":"crossref","unstructured":"He, Z., Liu, L., Zhu, Y., and Zhou, S. (2015, January 2\u20135). Anisotropically foveated nonlocal weights for joint sparse representation-based hyperspectral classification. Proceedings of the 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan.","key":"ref_15","DOI":"10.1109\/WHISPERS.2015.8075458"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/JSTARS.2015.2394330","article-title":"Hyperspectral image classification by spatial\u2013spectral derivative-aided kernel joint sparse representation","volume":"8","author":"Wang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.1109\/JSTARS.2016.2526604","article-title":"Adaptive nonlocal spatial-spectral kernel for hyperspectral imagery classification","volume":"9","author":"Wang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, G., Cao, M., and Jiang, N. (2016, January 21\u201324). Semi-supervised classification of hyperspectral image based on spectral and extended morphological profiles. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","key":"ref_18","DOI":"10.1109\/WHISPERS.2016.8071701"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"5039","DOI":"10.1109\/TGRS.2011.2157166","article-title":"Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification","volume":"49","author":"Shen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Jia, S., Xie, Y., Shen, L., and Deng, L. (2015, January 2\u20135). Hyperspectral image classification using Fisher criterion-based Gabor cube selection and multi-task joint sparse representation. Proceedings of the 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan.","key":"ref_21","DOI":"10.1109\/WHISPERS.2015.8075364"},{"doi-asserted-by":"crossref","unstructured":"Ye, Z., Bai, L., and Tan, L. (2017, January 2\u20134). Hyperspectral image classification based on gabor features and decision fusion. Proceedings of the 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China.","key":"ref_22","DOI":"10.1109\/ICIVC.2017.7984602"},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral image classification with deep feature fusion network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral-spatial 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_27","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_28","doi-asserted-by":"crossref","first-page":"179575","DOI":"10.1109\/ACCESS.2020.3027776","article-title":"Rotation equivariant convolutional neural networks for hyperspectral image classification","volume":"8","author":"Paoletti","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1109\/LGRS.2020.3010837","article-title":"ClusterCNN: Clustering-based feature learning for hyperspectral image classification","volume":"18","author":"Yao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5813","DOI":"10.1109\/TGRS.2019.2902568","article-title":"Hyperspectral classification based on lightweight 3-D-CNN with transfer learning","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8754","DOI":"10.1109\/TGRS.2021.3049377","article-title":"NAS-guided lightweight multi-scale attention fusion network for hyperspectral image classification","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"Multi-Structure KELM With Attention Fusion Strategy for Hyperspectral Image Classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.1109\/TIP.2022.3193747","article-title":"Spectral\u2013Spatial Latent Reconstruction for Open-Set Hyperspectral Image Classification","volume":"31","author":"Yue","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","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":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","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."},{"doi-asserted-by":"crossref","unstructured":"Zheng, Z., and Zhong, Y. (August, January 28). S3NET: Towards real-time hyperspectral imagery classification. Proceedings of the IEEE 2019 International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan.","key":"ref_36","DOI":"10.1109\/IGARSS.2019.8899261"},{"doi-asserted-by":"crossref","unstructured":"Tong, X., Yin, J., Han, B., and Qv, H. (2020, January 25\u201328). Few-shot learning with attention-weighted graph convolutional networks for hyperspectral image classification. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","key":"ref_37","DOI":"10.1109\/ICIP40778.2020.9190752"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8246","DOI":"10.1109\/TGRS.2020.2973363","article-title":"Nonlocal graph convolutional networks for hyperspectral image classification","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/JSTARS.2020.2968179","article-title":"Spectral-spatial 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_40","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/LGRS.2017.2786272","article-title":"Classification of hyperspectral imagery using a new fully convolutional neural network","volume":"15","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1109\/LGRS.2019.2945546","article-title":"Semisupervised classification based on SLIC segmentation for hyperspectral image","volume":"17","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"unstructured":"Chi, M., and Bruzzone, L. (2007, January 23\u201328). Classification of hyperspectral data by continuation semi-supervised SVM. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","key":"ref_43"},{"unstructured":"Bruzzone, L., Chi, M., and Marconcini, M. (2005, January 29). Transductive SVMs for semi-supervised classification of hyperspectral data. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Republic of Korea.","key":"ref_44"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/LGRS.2018.2869563","article-title":"Spectral\u2013spatial graph convolutional networks for semi-supervised hyperspectral image classification","volume":"16","author":"Qin","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"doi-asserted-by":"crossref","unstructured":"Zhan, Y., Medjadba, Y., Wang, G., Yu, X., Qin, J., Huang, T., Wu, K., Hu, D., Zhao, Z., and Wang, Y. (August, January 28). Hyperspectral image classification based on generative adversarial networks with feature fusing and dynamic neighborhood voting mechanism. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","key":"ref_46","DOI":"10.1109\/IGARSS.2019.8899291"},{"key":"ref_47","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_48","first-page":"1","article-title":"Dual-channel capsule generation adversarial network for hyperspectral image classification","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TBDATA.2019.2923243","article-title":"Beyond the patch-wise classification: Spectral-spatial fully convolutional networks for hyperspectral image classification","volume":"6","author":"Xu","year":"2019","journal-title":"IEEE Trans. Big Data."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1292\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:43:01Z","timestamp":1760121781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,26]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051292"],"URL":"https:\/\/doi.org\/10.3390\/rs15051292","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,26]]}}}