{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T03:36:56Z","timestamp":1764733016474,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T00:00:00Z","timestamp":1694476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundations of China","award":["42261075"],"award-info":[{"award-number":["42261075"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the continuous advancement of deep learning technology, researchers have made further progress in the hyperspectral image (HSI) classification domain. We propose a double-branch multi-scale residual network (DBMSRN) framework for HSI classification to improve classification accuracy and reduce the number of required training samples. The DBMSRN consists of two branches designed to extract spectral and spatial features from the HSI. Thus, to obtain more comprehensive feature information, we extracted additional local and global features at different scales by expanding the network width. Moreover, we also increased the network depth to capture deeper feature information. Based on this concept, we devise spectral multi-scale residuals and spatial multi-scale residuals within a double-branch architecture. Additionally, skip connections are employed to augment the context information of the network. We demonstrate that the proposed framework effectively enhances classification accuracy in scenarios with limited training samples through experimental analysis. The proposed framework achieves an overall accuracy of 98.67%, 98.09%, and 96.76% on the Pavia University (PU), Kennedy Space Center (KSC), and Indian Pines (IP) datasets, respectively, surpassing the classification accuracy of existing advanced frameworks under identical conditions.<\/jats:p>","DOI":"10.3390\/rs15184471","type":"journal-article","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T03:54:06Z","timestamp":1694490846000},"page":"4471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The Classification of Hyperspectral Images: A Double-Branch Multi-Scale Residual Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Laiying","family":"Fu","sequence":"first","affiliation":[{"name":"School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang 330013, China"},{"name":"School of Geomatics, East China University of Technology, Nanchang 330013, China"}]},{"given":"Xiaoyong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geomatics, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-037X","authenticated-orcid":false,"given":"Saied","family":"Pirasteh","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China"},{"name":"Department of Geotechnics and Geomatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India"}]},{"given":"Yanan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geomatics, East China University of Technology, Nanchang 330013, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral image data analysis as a high dimensional signal processing problem","volume":"19","author":"David","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5099","DOI":"10.3934\/mbe.2020275","article-title":"Research on land use classification of hyperspectral images based on multiscale superpixels","volume":"17","author":"Wang","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2014.11.001","article-title":"Urban land cover classification using airborne LiDAR data: A review","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1080\/07038992.2020.1823825","article-title":"Estimation of phytoplankton chlorophyll-a concentrations in the Western Basin of Lake Erie using Sentinel-2 and Sentinel-3 data","volume":"46","author":"Pirasteh","year":"2020","journal-title":"Can. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/JSTARS.2015.2489838","article-title":"Developing a spectral-based strategy for urban object detection from airborne hyperspectral TIR and visible data","volume":"9","author":"Eslami","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/JSTARS.2015.2406339","article-title":"Generation of spectral\u2013temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications","volume":"8","author":"Gevaert","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S10-90","DOI":"10.1179\/1432891715Z.0000000002096","article-title":"A method for recognising building materials based on hyperspectral remote sensing","volume":"19","author":"Ye","year":"2015","journal-title":"Mater. Res. Innov."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s10596-021-10033-6","article-title":"Deep learning for lithological classification of carbonate rock micro-CT images","volume":"25","author":"Avila","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/s41598-020-79864-0","article-title":"Application of hyperspectral remote sensing for supplementary investigation of polymetallic deposits in Huaniushan ore region, northwestern China","volume":"11","author":"Wan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102952","DOI":"10.1016\/j.earscirev.2019.102952","article-title":"Close-range, ground-based hyperspectral imaging for mining applications at various scales: Review and case studies","volume":"198","author":"Krupnik","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112129","DOI":"10.1016\/j.rse.2020.112129","article-title":"Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods","volume":"252","author":"Lorenz","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.procs.2015.08.025","article-title":"Comparative analysis of scattering and random features in hyperspectral image classification","volume":"58","author":"Haridas","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_14","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_15","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_16","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_17","doi-asserted-by":"crossref","unstructured":"Wang, W.-Y., Li, H.-C., Pan, L., Yang, G., and Du, Q. (2018, January 22\u201327). Hyperspectral image classification based on capsule network. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518951"},{"key":"ref_18","first-page":"0018","article-title":"Understanding belief propagation and its generalizations","volume":"8","author":"Yedidia","year":"2003","journal-title":"Explor. Artif. Intell. New Millenn."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","article-title":"Convolutional neural networks for hyperspectral image classification","volume":"219","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1109\/TIP.2018.2809606","article-title":"Diverse region-based CNN for hyperspectral image classification","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040273","article-title":"Feedback attention-based dense CNN for hyperspectral image classification","volume":"60","author":"Yu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/LGRS.2020.2988494","article-title":"Transferring CNN ensemble for hyperspectral image classification","volume":"18","author":"He","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep convolutional neural networks for hyperspectral image classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1109\/TLA.2019.9011544","article-title":"Hyperspectral Images Classification based on Inception Network and Kernel PCA","volume":"17","author":"Ruiz","year":"2019","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_27","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_28","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_29","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_30","doi-asserted-by":"crossref","unstructured":"Ma, W., Yang, Q., Wu, Y., Zhao, W., and Zhang, X. (2019). Double-branch multi-attention mechanism network for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11111307"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S., Duan, C., Yang, Y., and Wang, X. (2020). Classification of hyperspectral image based on double-branch dual-attention mechanism network. Remote Sens., 12.","DOI":"10.20944\/preprints201912.0059.v2"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018). A fast dense spectral\u2013spatial convolution network framework for hyperspectral images classification. Remote Sens., 10.","DOI":"10.3390\/rs10071068"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/JSTARS.2019.2915272","article-title":"Noise-robust hyperspectral image classification via multi-scale total variation","volume":"12","author":"Duan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/JSTARS.2020.3011992","article-title":"3-D channel and spatial attention based multiscale spatial\u2013spectral residual network for hyperspectral image classification","volume":"13","author":"Lu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wu, S., Zhang, J., and Zhong, C. (2019\u20132, January 28). Multiscale spectral-spatial unified networks for hyperspectral image classification. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900581"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pooja, K., Nidamanuri, R.R., and Mishra, D. (2019, January 24\u201326). Multi-scale dilated residual convolutional neural network for hyperspectral image classification. Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2019.8921284"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.neucom.2019.11.092","article-title":"Deep hybrid dilated residual networks for hyperspectral image classification","volume":"384","author":"Cao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4715","DOI":"10.1080\/01431160410001688295","article-title":"Geological application of Landsat ETM for mapping structural geology and interpretation: Aided by remote sensing and GIS","volume":"25","author":"Ali","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102120","DOI":"10.1016\/j.bspc.2020.102120","article-title":"A novel 3D medical image super-resolution method based on densely connected network","volume":"62","author":"Lu","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/JSTARS.2020.2983224","article-title":"A simplified 2D-3D CNN architecture for hyperspectral image classification based on spatial\u2013spectral fusion","volume":"13","author":"Yu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","unstructured":"Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv."},{"key":"ref_44","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, Lile, France."},{"key":"ref_45","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4471\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:49:08Z","timestamp":1760129348000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,12]]},"references-count":45,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184471"],"URL":"https:\/\/doi.org\/10.3390\/rs15184471","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,9,12]]}}}