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Province","award":["QL20220161"],"award-info":[{"award-number":["QL20220161"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["XDCX2022L024"],"award-info":[{"award-number":["XDCX2022L024"]}]},{"name":"Xiang-tan University","award":["2020YFA0713503"],"award-info":[{"award-number":["2020YFA0713503"]}]},{"name":"Xiang-tan University","award":["2022JJ30561"],"award-info":[{"award-number":["2022JJ30561"]}]},{"name":"Xiang-tan University","award":["2022-15"],"award-info":[{"award-number":["2022-15"]}]},{"name":"Xiang-tan University","award":["2023JJ30582"],"award-info":[{"award-number":["2023JJ30582"]}]},{"name":"Xiang-tan University","award":["QL20220161"],"award-info":[{"award-number":["QL20220161"]}]},{"name":"Xiang-tan University","award":["XDCX2022L024"],"award-info":[{"award-number":["XDCX2022L024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images.<\/jats:p>","DOI":"10.3390\/rs15133225","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T02:09:17Z","timestamp":1687399757000},"page":"3225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation"],"prefix":"10.3390","volume":"15","author":[{"given":"Lijing","family":"Bu","sequence":"first","affiliation":[{"name":"School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China"}]},{"given":"Dong","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China"}]},{"given":"Zhengpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China"}]},{"given":"Yin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China"},{"name":"National Center for Applied Mathematics in Hunan Laboratory, Xiangtan 411105, China"}]},{"given":"Mingjun","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3852","DOI":"10.1109\/JSTARS.2019.2903642","article-title":"Toward Efficient Land Cover Mapping: An Overview of the National Land Representation System and Land Cover Map 2015 of Bangladesh","volume":"12","author":"Jalal","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wang, N., Zheng, Z., Xia, J., Zhang, L., Zhang, X., Zhu, M., He, Y., Jiang, L., and Zhou, G. (2018, January 22\u201327). Monitoring of Drought Change in the Middle Reach of Yangtze River. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517595"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Goetzke, R., Braun, M., Thamm, H.P., and Menz, G. (2008, January 7\u201311). Monitoring and modeling urban land-use change with multitemporal satellitedata. Proceedings of the IGARSS 2008\u20142008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779770"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Darweesh, M., Mansoori, S.A., and Alahmad, H. (2019, January 5\u20137). Simple Roads Extraction Algorithm Based on Edge Detection Using Satellite Images. Proceedings of the 2019 IEEE 4th International Conference on Image, Vision and Computing, ICIVC, Xiamen, China.","DOI":"10.1109\/ICIVC47709.2019.8981118"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kussul, N., Shelestov, A., Yailymova, H., Yailymov, B., Lavreniuk, M., and Ilyashenko, M. (October, January 26). Satellite Agricultural Monitoring in Ukraineat Country Level: World Bank Project. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324573"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Di, Y., Xu, X., and Zhang, G. (2020, January 6\u20138). Research on secondary analysis method of synchronous satellite monitoring data of power grid wildfire. Proceedings of the 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA, Chongqing, China.","DOI":"10.1109\/ICIBA50161.2020.9277047"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, M., Duan, Z., Lan, Z., and Yi, S. (2023). Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision. Appl. Sci., 13.","DOI":"10.3390\/app13116407"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"439","DOI":"10.11834\/jrs.20210283","article-title":"Development of hyperspectral imaging remote sensing technology","volume":"25","author":"Liu","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8372","DOI":"10.1109\/TGRS.2020.2987400","article-title":"Satellite Video Super-Resolution Based on Adaptively Spatiotemporal Neighbors and Nonlocal Similarity Regularization","volume":"58","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TIP.2005.854479","article-title":"Super-resolution reconstruction of hyperspectral images","volume":"14","author":"Akgun","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, H., Yu, J., and Sun, W. (2014, January 4\u20139). Super-resolution mapping via multi-dictionary based sparse representation. Proceedings of the 2014 IEEE InternationalConference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854256"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, X., Han, Z., and He, S. (2017). Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization. Remote Sens., 9.","DOI":"10.3390\/rs9121286"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 12\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., and Ukita, N. (2018, January 18\u201323). Deep Back-Projection Networks for Super-Resolution. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00179"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.-T., and Zhang, L. (2019, January 15\u201320). Second-Order Attention Network for Single Image Super-Resolution. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01132"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1109\/JSTARS.2017.2655112","article-title":"Hyperspectral Image Superresolution by Transfer Learning","volume":"10","author":"Yuan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","unstructured":"Gomez, R.B., Jazaeri, A., and Kafatos, M. (2001). Geo-Spatial Image and Data Exploitation II, SPIE."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3230","DOI":"10.1109\/TGRS.2007.901007","article-title":"Improving Component Substitution Pansharpening Through Multivariate Regression of MS ++Pan Data","volume":"45","author":"Aiazzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1109\/TGRS.2014.2381272","article-title":"Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation","volume":"53","author":"Wei","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Shafait, F., and Mian, A. (2015, January 7\u201312). Bayesian sparse representation for hyperspectral image super resolution. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298986"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion","volume":"50","author":"Yokoya","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8028","DOI":"10.1109\/TIP.2020.3009830","article-title":"A Truncated Matrix Decomposition for Hyperspectral Image Super-Resolution","volume":"29","author":"Liu","year":"2020","journal-title":"IEEE Trans. Image Process"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.1109\/TIP.2019.2893530","article-title":"Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution","volume":"28","author":"Xu","year":"2019","journal-title":"IEEE Trans. Image Process"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1109\/LGRS.2017.2668299","article-title":"Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network","volume":"14","author":"Palsson","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qu, Y., Qi, H., and Kwan, C. (2018, January 18\u201323). Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00266"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1109\/TGRS.2020.3006534","article-title":"Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution","volume":"59","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TCI.2020.2996075","article-title":"Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery","volume":"6","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_31","first-page":"5503113","article-title":"Hyperspectral Image Super-Resolution via Recurrent Feedback Embedding and Spatial\u2013Spectral Consistency Regularization","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, J., Zhao, M., and Li, Y. (2019). Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation. Remote Sens., 11.","DOI":"10.3390\/rs11101229"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mei, S., Yuan, X., Ji, J., Zhang, Y., Wan, S., and Du, Q. (2017). Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9111139"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Q., Wang, Q., and Li, X. (2020). Mixed 2D\/3D Convolutional Network for Hyperspectral Image Super-Resolution. Remote Sens., 12.","DOI":"10.3390\/rs12101660"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11276","DOI":"10.1109\/TIE.2020.3038096","article-title":"Hyperspectral Image Superresolution Using Spectrum and Feature Context","volume":"68","author":"Wang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5504615","DOI":"10.1109\/TGRS.2023.3250640","article-title":"Diffused Convolutional Neural Network for Hyperspectral Image Super-Resolution","volume":"61","author":"Jia","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1109\/LGRS.2017.2737637","article-title":"Hyperspectral Image Super-Resolution by Spectral Difference Learning and Spatial Error Correction","volume":"14","author":"Hu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7459","DOI":"10.1109\/TGRS.2020.2982940","article-title":"Hyperspectral Image Super-Resolution via Intrafusion Network","volume":"58","author":"Hu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, L., Dingl, C., Wei, W., and Zhang, Y. (2018, January 13\u201316). Single Hyperspectral Image Super-Resolution with Grouped Deep Recursive Residual Network. Proceedings of the 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), Xi\u2019an, China.","DOI":"10.1109\/BigMM.2018.8499097"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5059","DOI":"10.1080\/01431161.2022.2128701","article-title":"Hyperspectral image super-resolution based on attention ConvBiLSTM network","volume":"43","author":"Lu","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neucom.2018.08.042","article-title":"3D separable convolutional neural network for dynamic hand gesture recognition","volume":"318","author":"Hu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., and Mei, T. (2017, January 22\u201329). Learning spatio-temporal representationwith pseudo-3D residual networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.590"},{"key":"ref_43","unstructured":"Hou, J., Zhu, Z., Hou, J., Liu, H., Zeng, H., and Meng, D. (2023). Deep Diversity-Enhanced Feature Representation of Hyperspectral Images. arXiv preprint."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TIP.2010.2046811","article-title":"Generalized Assorted Pixel Camera: Postcapture Control of Resolution, Dynamic Range, and Spectrum","volume":"19","author":"Yasuma","year":"2010","journal-title":"IEEE Trans. Image Process"},{"key":"ref_46","unstructured":"Yokoya, N., and Iwasaki, A. (2016). Airborne Hyperspectral Data Over Chikusei, University of Tokyo."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","first-page":"147","article-title":"Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm","volume":"Volume 1","author":"Yuhas","year":"1992","journal-title":"Proceedings of the JPL, Summaries of the Third Annual JPL Airborne Geoscience Workshop"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3225\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:58:20Z","timestamp":1760126300000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,21]]},"references-count":48,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133225"],"URL":"https:\/\/doi.org\/10.3390\/rs15133225","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,21]]}}}