{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T00:54:08Z","timestamp":1768697648488,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,16]],"date-time":"2020-03-16T00:00:00Z","timestamp":1584316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, deep learning methods have been widely used in the hyperspectral image (HSI) classification tasks. Among them, spectral-spatial combined methods based on the three-dimensional (3-D) convolution have shown good performance. However, because of the three-dimensional convolution, increasing network depth will result in a dramatic rise in the number of parameters. In addition, the previous methods do not make full use of spectral information. They mostly use the data after dimensionality reduction directly as the input of networks, which result in poor classification ability in some categories with small numbers of samples. To address the above two issues, in this paper, we designed an end-to-end 3D-ResNeXt network which adopts feature fusion and label smoothing strategy further. On the one hand, the residual connections and split-transform-merge strategy can alleviate the declining-accuracy phenomenon and decrease the number of parameters. We can adjust the hyperparameter cardinality instead of the network depth to extract more discriminative features of HSIs and improve the classification accuracy. On the other hand, in order to improve the classification accuracies of classes with small numbers of samples, we enrich the input of the 3D-ResNeXt spectral-spatial feature learning network by additional spectral feature learning, and finally use a loss function modified by label smoothing strategy to solve the imbalance of classes. The experimental results on three popular HSI datasets demonstrate the superiority of our proposed network and an effective improvement in the accuracies especially for the classes with small numbers of training samples.<\/jats:p>","DOI":"10.3390\/s20061652","type":"journal-article","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T08:20:44Z","timestamp":1584519644000},"page":"1652","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Three-Dimensional ResNeXt Network Using Feature Fusion and Label Smoothing for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6651-7921","authenticated-orcid":false,"given":"Peida","family":"Wu","sequence":"first","affiliation":[{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9699-3040","authenticated-orcid":false,"given":"Ziguan","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]},{"given":"Zongliang","family":"Gan","sequence":"additional","affiliation":[{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,16]]},"reference":[{"key":"ref_1","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."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Notesco, G., Ben Dor, E., and Brook, A. (2014, January 24\u201327). Mineral mapping of makhtesh ramon in israel using hyperspectral remote sensing day and night LWIR images. Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077538"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Villa, P., Pepe, M., Boschetti, M., and de Paulis, R. (2011, January 24\u201329). Spectral mapping capabilities of sedimentary rocks using hyperspectral data in Sicily, Italy. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049741"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Foucher, P.-Y., Poutier, L., D\u00e9liot, P., Puckrin, E., and Chataing, S. (2016, January 10\u201315). Hazardous and Noxious Substance detection by hyperspectral imagery for marine pollution application. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7731006"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, K., Cheng, T., Deng, X., Yao, X., Tian, Y., Zhu, Y., and Cao, W. (2016, January 21\u201324). Assessment of spectral variation between rice canopy components using spectral feature analysis of near-ground hyperspectral imaging data. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071699"},{"key":"ref_6","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Springer Science & Business Media."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hossain, M.A., Ahmed, B., and Mamun, M.A. (2017, January 16\u201318). Feature mining for effective subspace detection and classification of hyperspectral images. Proceedings of the 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox's Bazar, Bangladesh.","DOI":"10.1109\/ECACE.2017.7912965"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25069","DOI":"10.1109\/ACCESS.2017.2766242","article-title":"Feature Extraction Based Multi-Structure Manifold Embedding for Hyperspectral Remote Sensing Image Classification","volume":"5","author":"Gan","year":"2017","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in spectral-spatial classification of hyperspectral images","volume":"101","author":"Fauvel","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15224","DOI":"10.1109\/ACCESS.2018.2799079","article-title":"Fusion of Weighted Mean Reconstruction and SVMCK for Hyperspectral Image Classification","volume":"6","author":"Huang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"046505","DOI":"10.1117\/1.JRS.13.046505","article-title":"Partial informational correlation-based band selection for hyperspectral image classification","volume":"13","author":"Paul","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TGRS.2008.922034","article-title":"Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles","volume":"46","author":"Fauvel","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"9173","article-title":"Bilateral texture filtering for spectral-spatial hyperspectral image classification","volume":"2019","author":"Zhang","year":"2019","journal-title":"J. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6884","DOI":"10.1109\/TGRS.2018.2845450","article-title":"Tensor-Based Classification Models for Hyperspectral Data Analysis","volume":"56","author":"Makantasis","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Voulodimos, A., Doulamis, A., Doulamis, N., and Georgoulas, I. (2019, January 22\u201325). Hyperspectral Image Classification with Tensor-Based Rank-R Learning Models. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803268"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, Y., Zhang, X., Gao, H., Yang, Y., and Wang, J. (2019). Deep belief network for spectral\u2013spatial classification of hyperspectral remote sensor data. Sensors, 19.","DOI":"10.3390\/s19010204"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1080\/01431161.2015.1055607","article-title":"Hyperspectral classification via deep networks and superpixel segmentation","volume":"36","author":"Liu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"048502","DOI":"10.1117\/1.JRS.13.048502","article-title":"Bisupervised network with pyramid pooling module for land cover classification of satellite remote sensing imagery","volume":"13","author":"Li","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"61534","DOI":"10.1109\/ACCESS.2019.2916095","article-title":"Adaptive spatial-spectral feature learning for hyperspectral image classification","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","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_24","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_25","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_26","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_27","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_28","doi-asserted-by":"crossref","first-page":"84876","DOI":"10.1109\/ACCESS.2019.2925283","article-title":"SSDC-DenseNet: A Cost-Effective End-to-End Spectral-Spatial Dual-Channel Dense Network for Hyperspectral Image Classification","volume":"7","author":"Bai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","first-page":"7352","article-title":"Spectral\u2013spatial classification of hyperspectral remote sensing image based on capsule network","volume":"2019","author":"Jia","year":"2019","journal-title":"J. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Feng, F., Wang, S., Wang, C., and Zhang, J. (2019). Learning Deep Hierarchical Spatial\u2013Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN. Sensors, 19.","DOI":"10.3390\/s19235276"},{"key":"ref_31","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_32","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_33","doi-asserted-by":"crossref","unstructured":"Li, S., Zhu, X., and Bao, J. (2019). Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification. Sensors, 19.","DOI":"10.3390\/s19071714"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Ma, L., Jiang, H., and Zhao, H. (2017, January 23\u201328). Deep residual networks for hyperspectral image classification. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127330"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21-26). 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_37","doi-asserted-by":"crossref","first-page":"016519","DOI":"10.1117\/1.JRS.13.016519","article-title":"Three-dimensional densely connected convolutional network for hyperspectral remote sensing image classification","volume":"13","author":"Zhang","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/TGRS.2018.2871782","article-title":"Capsule networks for hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_41","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. ArXiv Prepr. ArXiv150203167."},{"key":"ref_42","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in neural information processing systems, Harrahs and Harveys, Lake Tahoe, CA, USA."},{"key":"ref_43","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., and Li, M. (2019, January 16\u201320). Bag of tricks for image classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00065"},{"key":"ref_45","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_46","unstructured":"M\u00fcller, R., Kornblith, S., and Hinton, G.E. (2019, January 8\u201314). When does label smoothing help?. Proceedings of the Advances in Neural Information Processing Systems, Vancouver Convention Center, Vancouver, Canada."},{"key":"ref_47","unstructured":"Computational Intelligence Group of the Basque University (UPV\/EHU) (2019, October 20). Hyperspectral Remote Sensing Scenes. Available online: http:\/\/www.ehu.eus\/ccwintco\/index.php\/Hyperspectral_Remote_Sensing_Scenes."},{"key":"ref_48","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_49","first-page":"5893","article-title":"Spectral\u2013spatial unified networks for hyperspectral image classification","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1652\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:07:16Z","timestamp":1760173636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1652"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,16]]},"references-count":49,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20061652"],"URL":"https:\/\/doi.org\/10.3390\/s20061652","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,16]]}}}