{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T22:59:59Z","timestamp":1773269999443,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T00:00:00Z","timestamp":1585094400000},"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":["61922013,61703287,61902339"],"award-info":[{"award-number":["61922013,61703287,61902339"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaoning Provincial Natural Science Foundation of China","award":["20180550337,2019-MS-254,20180550664"],"award-info":[{"award-number":["20180550337,2019-MS-254,20180550664"]}]},{"name":"Scientific Research Program of Liaoning Provincial Education Department of China","award":["JYT19029,L201726"],"award-info":[{"award-number":["JYT19029,L201726"]}]},{"name":"Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data, Yanan University","award":["IPBED14"],"award-info":[{"award-number":["IPBED14"]}]},{"name":"Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data, Yanan University","award":["IPBED14"],"award-info":[{"award-number":["IPBED14"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning has attracted extensive attention in the field of hyperspectral images (HSIs) classification. However, supervised deep learning methods heavily rely on a large amount of label information. To address this problem, in this paper, we propose a two-stage deep domain adaptation method for hyperspectral image classification, which can minimize the data shift between two domains and learn a more discriminative deep embedding space with very few labeled target samples. A deep embedding space is first learned by minimizing the distance between the source domain and the target domain based on Maximum Mean Discrepancy (MMD) criterion. The Spatial\u2013Spectral Siamese Network is then exploited to reduce the data shift and learn a more discriminative deep embedding space by minimizing the distance between samples from different domains but the same class label and maximizes the distance between samples from different domains and class labels based on pairwise loss. For the classification task, the softmax layer is replaced with a linear support vector machine, in which learning minimizes a margin-based loss instead of the cross-entropy loss. The experimental results on two sets of hyperspectral remote sensing images show that the proposed method can outperform several state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs12071054","type":"journal-article","created":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T13:10:47Z","timestamp":1585141847000},"page":"1054","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Two-stage Deep Domain Adaptation Method for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhaokui","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Xiangyi","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6737-3472","authenticated-orcid":false,"given":"Chuanyun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Cuiwei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Jinrong","family":"He","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Yan\u2019an University, Yan\u2019an 716000, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,25]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Laurin, G.V., Chan, J.C.W., Chen, Q., Lindsell, J.A., Coomes, D.A., Guerriero, L., Del Frate, F., Miglietta, F., and Valentini, R. (2014). Biodiversity mapping in a tropical West African forest with airborne hyperspectral data. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0097910"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yokoya, N., Chan, J.C.W., and Segl, K. (2016). Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images. Remote Sens., 8.","DOI":"10.3390\/rs8030172"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1109\/TCYB.2014.2362959","article-title":"Semi-Supervised Multitask Learning for Scene Recognition","volume":"45","author":"Lu","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_5","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_6","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":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Li, Z., Huang, L., and He, J. (2019). A Multiscale Deep Middle-level Feature Fusion Network for Hyperspectral Classification. Remote Sens., 11.","DOI":"10.3390\/rs11060695"},{"key":"ref_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning multiscale and deep representations for classifying remotely sensed imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1109\/TGRS.2007.895416","article-title":"Semi-supervised graph-based hyperspectral image classification","volume":"45","author":"Marsheva","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/LGRS.2017.2711425","article-title":"Active and semisupervised learning with morphological component analysis for hyperspectral image classification","volume":"14","author":"Zhou","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/TGRS.2016.2627042","article-title":"Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification","volume":"55","author":"Ye","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5585","DOI":"10.1109\/TGRS.2017.2710079","article-title":"Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification","volume":"55","author":"Jiao","year":"2017","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":"4729","DOI":"10.1109\/TGRS.2017.2698503","article-title":"Learning and Transferring Deep Joint Spectral\u2013Spatial Features for Hyperspectral Classification","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MGRS.2016.2548504","article-title":"Domain adaptation for the classification of remote sensing data: An overview of recent advances","volume":"4","author":"Tuia","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_19","unstructured":"Long, M., Zhu, H., Wang, J., and Jordan, M.I. (2017, January 6\u201311). Deep Transfer Learning with Joint Adaptation Networks. Proceedings of the 2017 International Conference on Machine Learning (ICML), Sydney, NSW, Australia."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., and Zuo, W. (2017, January 21\u201326). Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.107"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1109\/LGRS.2012.2236818","article-title":"Learn multiple-kernel SVMs for domain adaptation in hyperspectral data","volume":"10","author":"Sun","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.patcog.2017.10.007","article-title":"Active multi-kernel domain adaptation for hyperspectral image classification","volume":"77","author":"Deng","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1109\/JSTARS.2015.2449738","article-title":"Domain adaptation with preservation of manifold geometry for hyperspectral image classification","volume":"9","author":"Yang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1109\/TGRS.2017.2692281","article-title":"Domain adaptation network for cross-scene classification","volume":"55","author":"Othman","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/LGRS.2018.2889967","article-title":"Domain Adaptation with Discriminative Distribution and Manifold Embedding for Hyperspectral Image Classification","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Motiian, S., Piccirilli, M., Adjeroh, D.A., and Doretto, G. (2017, January 22\u201329). Unified Deep Supervised Domain Adaptation and Generalization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref_27","unstructured":"Yichuan, T. (2015). Deep learning using linear support vector machines. arXiv."},{"key":"ref_28","unstructured":"Chopra, S., Hadsell, R., and LeCun, Y. (2005, January 20\u201326). Learning a Similarity Metric Discriminatively, with Application to Face Verification. Proceedings of the 2005 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_29","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 2015 International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/JSTARS.2015.2498664","article-title":"Fusion of spectral and spatial information for classification of hyperspectral remote-sensed imagery by local graph","volume":"9","author":"Liao","year":"2016","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Z., Huang, L., Zhang, D., Liu, C., Wang, Y., and Shi, X. (2018, January 17\u201319). A deep network based on multiscale spectral-spatial fusion for Hyperspectral Classification. Proceedings of the 2018 International Conference on Knowledge Science, Engineering and Management (KSEM), Jilin, China.","DOI":"10.1007\/978-3-319-99247-1_25"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3248","DOI":"10.1109\/TGRS.2016.2514404","article-title":"Support tensor machines for classification of hyperspectral remote sensing imagery","volume":"54","author":"Guo","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A.D., and Doulamis, N.D. (2015, January 26\u201331). Deep Supervised Learning for Hyperspectral Data Classification Through Convolutional Neural Networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J.A. (2014). A Method for Stochastic Optimization. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1054\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:11:16Z","timestamp":1760173876000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1054"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,25]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071054"],"URL":"https:\/\/doi.org\/10.3390\/rs12071054","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,25]]}}}