{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T11:19:26Z","timestamp":1772018366692,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62371362"],"award-info":[{"award-number":["62371362"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["2022GY-060"],"award-info":[{"award-number":["2022GY-060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the general project of the key R&amp;D Plan of Shaanxi Province","award":["62371362"],"award-info":[{"award-number":["62371362"]}]},{"name":"the general project of the key R&amp;D Plan of Shaanxi Province","award":["2022GY-060"],"award-info":[{"award-number":["2022GY-060"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning is an important research topic in the field of image super-resolution. Problematically, the performance of existing hyperspectral image super-resolution networks is limited by feature learning for hyperspectral images. Nevertheless, the current algorithms exhibit some limitations in extracting diverse features. In this paper, we address limitations to existing hyperspectral image super-resolution networks, focusing on feature learning challenges. We introduce the Channel-Attention-Based Spatial\u2013Spectral Feature Extraction network (CSSFENet) to enhance hyperspectral image feature diversity and optimize network loss functions. Our contributions include: (a) a convolutional neural network super-resolution algorithm incorporating diverse feature extraction to enhance the network\u2019s diversity feature learning by elevating the matrix rank, (b) a three-dimensional (3D) feature extraction convolution module, the Channel-Attention-Based Spatial\u2013Spectral Feature Extraction Module (CSSFEM), to boost the network\u2019s performance in both the spatial and spectral domains, (c) a feature diversity loss function designed based on the image matrix\u2019s singular value to maximize element independence, and (d) a spatial\u2013spectral gradient loss function introduced based on space and spectrum gradient values to enhance the reconstructed image\u2019s spatial\u2013spectral smoothness. In contrast to existing hyperspectral super-resolution algorithms, we used four evaluation indexes, PSNR, mPSNR, SSIM, and SAM, and our method showed superiority during testing with three common hyperspectral datasets.<\/jats:p>","DOI":"10.3390\/rs16030436","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T04:17:40Z","timestamp":1705983460000},"page":"436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hyperspectral Image Super-Resolution Based on Feature Diversity Extraction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8495-2804","authenticated-orcid":false,"given":"Jing","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of lntegrated Service Network, Xidian University, Xi\u2019an 710071, China"},{"name":"School of Telecommunication Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510700, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renjie","family":"Zheng","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zekang","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Telecommunication Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruijing","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Space Information, Space Engineering University, Beijing 101416, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"System Engineering Research Institute of CSSC, Beijing 100070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"System Engineering Research Institute of CSSC, Beijing 100070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuepeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Telecommunication Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunsong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of lntegrated Service Network, Xidian University, Xi\u2019an 710071, China"},{"name":"School of Telecommunication Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"ref_1","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 International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854256"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Huang, H., Christodoulou, A.G., and Sun, W. (2014, January 27\u201330). Super-resolution hyperspectral imaging with unknown blurring by low-rank and group-sparse modeling. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025432"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4784","DOI":"10.1109\/JSTARS.2014.2328596","article-title":"Remote sensing image super-resolution reconstruction based on nonlocal pairwise dictionaries and double regularization","volume":"7","author":"Gou","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/LGRS.2016.2579661","article-title":"Hyperspectral image super-resolution by spectral mixture analysis and spatial\u2014Spectral group sparsity","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1109\/JSTARS.2017.2655112","article-title":"Hyperspectral image super-resolution by transfer learning","volume":"10","author":"Yuan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"4304","DOI":"10.1109\/TGRS.2019.2962713","article-title":"Hyperspectral image super-resolution by band attention through adversarial learning","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xie, S., Sun, C., Huang, J., Tu, Z., and Murphy, K. (2018, January 8\u201314). Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_19"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"8693","DOI":"10.1109\/TGRS.2020.3047363","article-title":"Exploring the relationship between 2D\/3D convolution for hyperspectral image super-resolution","volume":"59","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7711","DOI":"10.1109\/TGRS.2021.3049875","article-title":"A spectral grouping and attention-driven residual dense network for hyperspectral image super-resolution","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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_13","first-page":"5518317","article-title":"X-Shaped Interactive Autoencoders with Cross-Modality Mutual Learning for Unsupervised Hyperspectral Image Super-Resolution","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"5509417","article-title":"Enhanced Autoencoders with Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution","volume":"61","author":"Gao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"5508605","article-title":"Model-Guided Coarse-to-Fine Fusion Network for Unsupervised Hyperspectral Image Super-Resolution","volume":"20","author":"Li","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shao, M., Wan, Z., and Li, Y. (2021). Multiscale Feature Mapping Network for Hyperspectral Image Super-Resolution. Remote Sens., 13.","DOI":"10.3390\/rs13204180"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, R., Chen, X., Hong, Z., Li, Y., and Lu, R. (2023). Spectral Correlation and Spatial High\u2013Low Frequency Information of Hyperspectral Image Super-Resolution Network. Remote Sens., 15.","DOI":"10.3390\/rs15092472"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ju, Y., Jian, M., Wang, C., Zhang, C., Dong, J., and Lam, K.-M. (IEEE Trans. Circuits Syst. Video Technol., 2023). Estimating High-resolution Surface Normals via Low-resolution Photometric Stereo Images, IEEE Trans. Circuits Syst. Video Technol., early access.","DOI":"10.1109\/TCSVT.2023.3301930"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, Y., Gu, J., Kong, L., Yang, X., and Yu, F. (2023, January 4\u20136). Dual Aggregation Transformer for Image Super-Resolution. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01131"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xia, R., Yang, K., and Zou, K. (2023). MFFN: Image super-resolution via multi-level features fusion network. Vis. Comput., 1\u201316.","DOI":"10.1007\/s00371-023-02795-0"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TCYB.2016.2605044","article-title":"Simultaneous spectral-spatial feature selection and extraction for hyperspectral images","volume":"48","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Heide, F., Heidrich, W., and Wetzstein, G. (2015, January 7\u201312). Fast and flexible convolutional sparse coding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299149"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yanai, K., Tanno, R., and Okamoto, K. (2016, January 15\u201319). Efficient mobile implementation of a cnn-based object recognition system. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967243"},{"key":"ref_24","unstructured":"Denil, M., Shakibi, B., Dinh, L., Ranzato, M.A., and De Freitas, N. (2013). Predicting parameters in deep learning. Adv. Neural Inf. Process. Syst., 26."},{"key":"ref_25","unstructured":"Shang, W., Sohn, K., Almeida, D., and Lee, H. (2016, January 20\u201322). Understanding and improving convolutional neural networks via concatenated rectified linear units. Proceedings of the International Conference on Machine Learning, PMLR, New York, NY, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, M., Ji, R., Wang, Y., Zhang, Y., Zhang, B., Tian, Y., and Shao, L. (2020, January 13\u201319). Hrank: Filter pruning using high-rank feature map. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5720","DOI":"10.1109\/TIP.2022.3201478","article-title":"Deep posterior distribution-based embedding for hyperspectral image super-resolution","volume":"31","author":"Hou","year":"2022","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/436\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:47:40Z","timestamp":1760104060000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/436"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,23]]},"references-count":29,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16030436"],"URL":"https:\/\/doi.org\/10.3390\/rs16030436","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,23]]}}}