{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:54:17Z","timestamp":1740149657148,"version":"3.37.3"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Programs in Science and Technology, China","award":["202202AD080004"]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62061049","12263008"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The extraction of effective classification features from high-dimensional hyperspectral images, impeded by the scarcity of labeled samples and uneven sample distribution, represents a formidable challenge within hyperspectral image classification. Traditional few-shot learning methods confront the dual dilemma of limited annotated samples and the necessity for deeper, more effective features from complex hyperspectral data, often resulting in suboptimal outcomes. The prohibitive cost of sample annotation further exacerbates the challenge, making it difficult to rely on a scant number of annotated samples for effective feature extraction. Prevailing high-accuracy algorithms require abundant annotated samples and falter in deriving deep, discriminative features from limited data, compromising classification performance for complex substances. This paper advocates for an integration of advanced spectral\u2013spatial feature extraction with meta-transfer learning to address the classification of hyperspectral signals amidst insufficient labeled samples. Initially trained on a source domain dataset with ample labels, the model undergoes transference to a target domain with minimal samples, utilizing dense connection blocks and tree-dimensional convolutional residual connections to enhance feature extraction and maximize spatial and spectral information retrieval. This approach, validated on three diverse hyperspectral datasets\u2014IP, UP, and Salinas\u2014significantly surpasses existing classification algorithms and small-sample techniques in accuracy, demonstrating its applicability to high-dimensional signal classification under label constraints.<\/jats:p>","DOI":"10.3390\/s24092664","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T07:20:41Z","timestamp":1713856841000},"page":"2664","source":"Crossref","is-referenced-by-count":2,"title":["Transfer Learning-Based Hyperspectral Image Classification Using Residual Dense Connection Networks"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3150-7029","authenticated-orcid":false,"given":"Hao","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650504, China"}]},{"given":"Xianwang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650504, China"}]},{"given":"Kunming","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650504, China"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"Yunnan Power Grid Co., Ltd., Kunming 650011, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8449-6861","authenticated-orcid":false,"given":"Guowu","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650504, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, G., Cao, W., and Wei, Y. (2022). Spatial perception correntropy matrix for hyperspectral image classification. Appl. Sci., 12.","DOI":"10.3390\/app12136797"},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1109\/TGRS.2016.2646420","article-title":"Estimating soil salinity under various moisture conditions: An experimental study","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4118","DOI":"10.1109\/TIP.2018.2836307","article-title":"Fusing hyperspectral and multispectral images via coupled sparse tensor factorization","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5767","DOI":"10.1109\/TGRS.2018.2825457","article-title":"Spatial discontinuity-weighted sparse unmixing of hyperspectral images","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","first-page":"689","article-title":"Current progress of hyperspectral remote sensing in China","volume":"20","author":"Tong","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1109\/TGRS.2016.2642951","article-title":"Three-dimensional local binary patterns for hyperspectral imagery classification","volume":"55","author":"Jia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.patrec.2018.08.032","article-title":"A spatial-spectral SIFT for hyperspectral image matching and classification","volume":"127","author":"Li","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_11","first-page":"5910","article-title":"Optimal clustering framework for hyperspectral band selection","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1109\/TGRS.2018.2862899","article-title":"Locality and structure regularized low rank representation for hyperspectral image classification","volume":"57","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"4099","article-title":"Local manifold learning-based k-nearest-neighbor for hyperspectral image classification","volume":"48","author":"Ma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kuo, B.C., Huang, C.S., Hung, C.C., Liu, Y.L., and Chen, I.L. (2010, January 25\u201330). Spatial information based support vector machine for hyperspectral image classification. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5651433"},{"key":"ref_15","unstructured":"Ren, Y., Zhang, Y., and Li, L. (2014, January 8\u20139). A spectral-spatial hyperspectral data classification approach using random forest with label constraints. Proceedings of the 2014 IEEE Workshop on Electronics, Computer and Applications, Ottawa, ON, Canada."},{"key":"ref_16","first-page":"1","article-title":"RanPaste: Paste consistency and pseudo label for semi-supervised remote sensing image semantic segmentation","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/j.dt.2021.08.001","article-title":"Ballistic response of armour plates using generative adversarial networks","volume":"18","author":"Thompson","year":"2022","journal-title":"Def. Technol."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/LGRS.2020.2979604","article-title":"Dual-path siamese CNN for hyperspectral image classification with limited training samples","volume":"18","author":"Huang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2019.08.018","article-title":"Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery","volume":"157","author":"Shendryk","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zheng, X., Jia, J., Chen, J., Guo, S., Sun, L., Zhou, C., and Wang, Y. (2022). Hyperspectral image classification with imbalanced data based on semi-supervised learning. Appl. Sci., 12.","DOI":"10.3390\/app12083943"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, Y., Tang, X., Zhang, X., Ma, J., Liu, F., Jia, X., and Jiao, L. (2022). Semi-supervised multiscale dynamic graph convolution network for hyperspectral image classification. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315.","DOI":"10.1109\/TNNLS.2022.3212985"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3854635","DOI":"10.1155\/2022\/3854635","article-title":"Hyperspectral image classification: Potentials, challenges, and future directions","volume":"2022","author":"Datta","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_26","first-page":"4508319","article-title":"A unified multiscale learning framework for hyperspectral image classification","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/JSTARS.2022.3145917","article-title":"Multiscale densely connected attention network for hyperspectral image classification","volume":"15","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","unstructured":"Dhillon, G.S., Chaudhari, P., Ravichandran, A., and Soatto, S. (2019). A baseline for few-shot image classification. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1007\/s11227-023-05539-y","article-title":"Color image encryption based on novel kolam scrambling and modified 2D logistic cascade map (2D LCM)","volume":"80","author":"Mathivanan","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Devabathini, N.J., and Mathivanan, P. (2023, January 14\u201316). Sign Language Recognition Through Video Frame Feature Extraction using Transfer Learning and Neural Networks. Proceedings of the 2023 International Conference on Next Generation Electronics (NEleX), Vellore, Tamil Nadu, India.","DOI":"10.1109\/NEleX59773.2023.10421383"},{"key":"ref_33","unstructured":"Koch, G., Zemel, R., and Salakhutdinov, R. (2015, January 6\u201311). Siamese neural networks for one-shot image recognition. Proceedings of the ICML Deep Learning Workshop, Lille, France."},{"key":"ref_34","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., and Wierstra, D. (2016, January 5\u201310). Matching networks for one shot learning. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_35","unstructured":"Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J.B., Larochelle, H., and Zemel, R.S. (2018). Meta-learning for semi-supervised few-shot classification. arXiv."},{"key":"ref_36","unstructured":"Munkhdalai, T., and Yu, H. (2017, January 6\u201311). Meta networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, Y., Chua, T.S., and Schiele, B. (2019, January 16\u201317). Meta-transfer learning for few-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yu, M., Guo, X., Yi, J., Chang, S., Potdar, S., Cheng, Y., Tesauro, G., Wang, H., and Zhou, B. (2018). Diverse few-shot text classification with multiple metrics. arXiv.","DOI":"10.18653\/v1\/N18-1109"},{"key":"ref_39","unstructured":"Liu, Y., Sun, Q., Liu, A.A., Su, Y., Schiele, B., and Chua, T.S. (2019). LCC: Learning to customize and combine neural networks for few-shot learning. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TGRS.2018.2872830","article-title":"Deep few-shot learning for hyperspectral image classification","volume":"57","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1109\/TIP.2024.3351443","article-title":"SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot Hyperspectral Image Classification","volume":"33","author":"Li","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","first-page":"5514505","article-title":"Cross-Domain Few-Shot Contrastive Learning for Hyperspectral Images Classification","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","unstructured":"Misra, D. (2019). Mish: A self regularized non-monotonic activation function. arXiv."},{"key":"ref_44","unstructured":"(2023, November 17). Indian Pines Dataset. Available online: https:\/\/www.ehu.eus\/ccwintco\/index.php\/Hyperspectral_Remote_Sensing_Scenes."},{"key":"ref_45","unstructured":"Yokoya, N., and Iwasaki, A. (2016). Airborne Hyperspectral Data over Chikusei, Space Application Laboratory, The University of Tokyo. SAL-2016-05-27; Technical Report."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going deeper with contextual CNN for hyperspectral image classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"016506","DOI":"10.1117\/1.JRS.16.016506","article-title":"Multiscale nested U-Net for small sample classification of hyperspectral images","volume":"16","author":"Liu","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/JSTARS.2023.3234302","article-title":"Convolutional Transformer-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification","volume":"16","author":"Peng","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1109\/TNNLS.2022.3185795","article-title":"Graph information aggregation cross-domain few-shot learning for hyperspectral image classification","volume":"35","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yang, L., Li, L., Zhang, Z., Zhou, X., Zhou, E., and Liu, Y. (2020, January 13\u201319). Dpgn: Distribution propagation graph network for few-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"ref_52","first-page":"5501618","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2664\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T19:08:35Z","timestamp":1736968115000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,23]]},"references-count":52,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24092664"],"URL":"https:\/\/doi.org\/10.3390\/s24092664","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,4,23]]}}}