{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:26:40Z","timestamp":1776886000863,"version":"3.51.2"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"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":["62107027, 61876152"],"award-info":[{"award-number":["62107027, 61876152"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M692006"],"award-info":[{"award-number":["2021M692006"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) classification has been marked by exceptional progress in recent years. Much of this progess has come from advances in convolutional neural networks (CNNs). Different from the RGB images, HSI images are captured by various remote sensors with different spectral configurations. Moreover, each HSI dataset only contains very limited training samples and thus the model is prone to overfitting when using deep CNNs. In this paper, we first propose a 3D asymmetric inception network, AINet, to overcome the overfitting problem. With the emphasis on spectral signatures over spatial contexts of HSI data, the 3D convolution layer of AINet is replaced with two asymmetric inception units, i.e., a space inception unit and spectrum inception unit, to convey and classify the features effectively. In addition, we exploited a data-fusion transfer learning strategy to improve model initialization and classification performance. Extensive experiments show that the proposed approach outperforms all of the state-of-the-art methods via several HSI benchmarks, including Pavia University, Indian Pines and Kennedy Space Center (KSC).<\/jats:p>","DOI":"10.3390\/rs14071711","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T21:23:55Z","timestamp":1648848235000},"page":"1711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Hyperspectral Image Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3461-7995","authenticated-orcid":false,"given":"Bei","family":"Fang","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi\u2019an 710062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"ByteDance, Singapore 148957, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haokui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Intellifusion, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juhou","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi\u2019an 710062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TCYB.2015.2453359","article-title":"Learning hierarchical spectral\u2013spatial features for hyperspectral image classification","volume":"46","author":"Zhou","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1109\/TCYB.2016.2533430","article-title":"Spectral\u2013spatial shared linear regression for hyperspectral image classification","volume":"47","author":"Yuan","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep learning classifiers for hyperspectral imaging: A review","volume":"158","author":"Paoletti","year":"2019","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TNNLS.2015.2477537","article-title":"Salient band selection for hyperspectral image classification via manifold ranking","volume":"27","author":"Wang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/TII.2012.2205397","article-title":"A new dimensionality reduction algorithm for hyperspectral image using evolutionary strategy","volume":"8","author":"Yin","year":"2012","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_7","first-page":"4034","article-title":"Double nearest proportion feature extraction for hyperspectral-image classification","volume":"48","author":"Huang","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1109\/TGRS.2008.2008308","article-title":"Kernel nonparametric weighted feature extraction for hyperspectral image classification","volume":"47","author":"Kuo","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/TGRS.2012.2209657","article-title":"Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features","volume":"51","author":"Qian","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1109\/TCYB.2017.2682846","article-title":"A 3-D Gabor phase-based coding and matching framework for hyperspectral imagery classification","volume":"48","author":"Jia","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1109\/TGRS.2014.2360672","article-title":"Hyperspectral image classification based on three-dimensional scattering wavelet transform","volume":"53","author":"Tang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (2016, January 11\u201314). Fully-convolutional siamese networks for object tracking. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Li, Y. (2016, January 18\u201320). Spectral-spatial classification of hyperspectral imagery based on deep convolutional network. Proceedings of the 2016 International Conference on Orange Technologies (ICOT), Melbourne, VIC, Australia.","DOI":"10.1109\/ICOT.2016.8278975"},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1109\/TGRS.2009.2016214","article-title":"Spectral\u2013spatial classification of hyperspectral imagery based on partitional clustering techniques","volume":"47","author":"Tarabalka","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_25","unstructured":"Xie, S., Sun, C., Huang, J., Tu, Z., and Murphy, K. (2017). Rethinking spatiotemporal feature learning for video understanding. arXiv."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1080\/2150704X.2017.1280200","article-title":"Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (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_29","doi-asserted-by":"crossref","unstructured":"Fang, B., Li, Y., Zhang, H., and Chan, J.C.W. (2019). Hyperspectral images classification based on dense convolutional networks with spectral-wise attention mechanism. Remote Sens., 11.","DOI":"10.3390\/rs11020159"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_31","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient CNN architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_33","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xiong, Z., Yuan, Y., and Wang, Q. (2018, January 22\u201327). AI-NET: Attention inception neural networks for hyperspectral image classification. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517365"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8717","DOI":"10.1080\/01431161.2020.1781286","article-title":"Data classification of hyperspectral images based on inception networks and extended attribute profiles","volume":"41","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_39","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.isprsjprs.2020.01.015","article-title":"Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples","volume":"161","author":"Fang","year":"2020","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_41","first-page":"1","article-title":"Depthwise separable ResNet in the MAP framework for hyperspectral image classification","volume":"19","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","first-page":"1","article-title":"A lightweight spectral-spatial convolution module for hyperspectral image classification","volume":"19","author":"Meng","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Quattoni, A., Collins, M., and Darrell, T. (2008, January 23\u201328). Transfer learning for image classification with sparse prototype representations. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587637"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Lin, J., Ward, R., and Wang, Z.J. (2018, January 29\u201331). Deep transfer learning for hyperspectral image classification. Proceedings of the 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, Canada.","DOI":"10.1109\/MMSP.2018.8547139"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3246","DOI":"10.1109\/TGRS.2019.2951445","article-title":"Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network","volume":"58","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1080\/2150704X.2020.1714772","article-title":"Classification of small-scale hyperspectral images with multi-source deep transfer learning","volume":"11","author":"Zhao","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_49","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_50","unstructured":"De Br\u00e9bisson, A., and Vincent, P. (2015). An exploration of softmax alternatives belonging to the spherical loss family. arXiv."},{"key":"ref_51","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2016.11.034","article-title":"Integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification","volume":"226","author":"Cao","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/0895-4356(88)90031-5","article-title":"A reappraisal of the kappa coefficient","volume":"41","author":"Thompson","year":"1988","journal-title":"J. Clin. Epidemiol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"H\u00e4nsch, R., Ley, A., and Hellwich, O. (2017, January 23\u201328). Correct and still wrong: The relationship between sampling strategies and the estimation of the generalization error. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127795"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.1109\/TGRS.2020.3048128","article-title":"Attention-based second-order pooling network for hyperspectral image classification","volume":"59","author":"Xue","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1711\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:43Z","timestamp":1760136523000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":55,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071711"],"URL":"https:\/\/doi.org\/10.3390\/rs14071711","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,1]]}}}