{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:53:14Z","timestamp":1760241194994,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,7]],"date-time":"2019-12-07T00:00:00Z","timestamp":1575676800000},"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>Hyperspectral imaging is capable of acquiring the rich spectral information of scenes and has great potential for understanding the characteristics of different materials in many applications ranging from remote sensing to medical imaging. However, due to hardware limitations, the existed hyper-\/multi-spectral imaging devices usually cannot obtain high spatial resolution. This study aims to generate a high resolution hyperspectral image according to the available low resolution hyperspectral and high resolution RGB images. We propose a novel hyperspectral image superresolution method via non-negative sparse representation of reflectance spectra with a data guided sparsity constraint. The proposed method firstly learns the hyperspectral dictionary from the low resolution hyperspectral image and then transforms it into the RGB one with the camera response function, which is decided by the physical property of the RGB imaging camera. Given the RGB vector and the RGB dictionary, the sparse representation of each pixel in the high resolution image is calculated with the guidance of a sparsity map, which measures pixel material purity. The sparsity map is generated by analyzing the local content similarity of a focused pixel in the available high resolution RGB image and quantifying the spectral mixing degree motivated by the fact that the pixel spectrum of a pure material should have sparse representation of the spectral dictionary. Since the proposed method adaptively adjusts the sparsity in the spectral representation based on the local content of the available high resolution RGB image, it can produce more robust spectral representation for recovering the target high resolution hyperspectral image. Comprehensive experiments on two public hyperspectral datasets and three real remote sensing images validate that the proposed method achieves promising performances compared to the existing state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s19245401","type":"journal-article","created":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T05:54:51Z","timestamp":1575870891000},"page":"5401","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spectral Representation via Data-Guided Sparsity for Hyperspectral Image Super-Resolution"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5003-3180","authenticated-orcid":false,"given":"Xian-Hua","family":"Han","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology for Innovation, Yamaguchi University, 1677-1 Yoshida, Yamaguchi 753-8511, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqing","family":"Sun","sequence":"additional","affiliation":[{"name":"Media Intelligence Lab, NTT Corporation, Kanagawa 239-0847, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu 525-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6749-0364","authenticated-orcid":false,"given":"Boxin","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinqiang","family":"Zheng","sequence":"additional","affiliation":[{"name":"National Institute of Informatics, Tokyo 101-8430, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yen-Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu 525-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,7]]},"reference":[{"key":"ref_1","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":"2013","journal-title":"Proc. IEEE"},{"key":"ref_2","unstructured":"Nguyen, H., Benerjee, A., and Chellappa, R. (2010, January 13\u201318). Tracking via object reflectance using a hyperspectral video camera. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\u2014Workshops, San Francisco, CA, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/TSMCB.2009.2037132","article-title":"Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers","volume":"40","author":"Tarabalka","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_4","first-page":"10","article-title":"Hyperspectral face recognition using 3D-DCT and partial least squares","volume":"1","author":"Uzair","year":"2013","journal-title":"BMVC"},{"key":"ref_5","first-page":"2:1","article-title":"A comparative study of palmprint recognition algorithm","volume":"44","author":"Zhang","year":"2012","journal-title":"ACM Comput. Aurv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Chang, H., Barner, K., Spellman, P., and Parvin, B. (2014, January 23\u201328). Classification of histology sections via multispectral convolutional sparse coding. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.394"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Shafait, F., and Mian, A. (2014, January 24\u201328). SUnGP: A greedy sparse approximation algorithm for hyperspectral unmixing. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.640"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Lanaras, C., Baltsavias, E., and Schindler, K. (2015, January 7\u201313). Hyperspectral superresolution by coupled spectral unmixing. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.409"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Grohnfeldt, C., Zhu, X.X., and Bamler, R. (2013, January 21\u201326). Jointly sparse fusion of hyperspectral and multispectral imagery. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium\u2014IGARSS, Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6723732"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Shafait, F., and Mian, A. (2015, January 7\u201313). Bayesian sparse representation for hyperspectral image super resolution. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/CVPR.2015.7298986"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Shafait, F., and Mian, A. (2014). Sparse Spatio-Spectral Representation for Hyperspectral Image Super-Resolution. European Conference on Computer Vision, Springer.","DOI":"10.1109\/CVPR.2015.7298986"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","article-title":"Image denoising via sparse and redundant representations over learned dictionaries","volume":"15","author":"Elad","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","article-title":"Signal Recovery from Random Measurements via Orthogonal Matching Pursuit","volume":"53","author":"Tropp","year":"2007","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1109\/TIT.2011.2173241","article-title":"Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit","volume":"58","author":"Donoho","year":"2012","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust face recognition via sparse representation","volume":"31","author":"Wright","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Han, X.H., Wang, J., Shi, B., Zheng, Y., and Chen, Y.W. (2017, January 11\u201313). Hyper-Spectral Image Super-Resolution Using Non-Negative Spectral Representation with Data-Guided Sparsity. Proceedings of the 2017 IEEE International Symposium on Multimedia (ISM), Taichung, Taiwan.","DOI":"10.1109\/ISM.2017.99"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TGRS.2013.2253612","article-title":"Spatial and spectral image fusion using sparse matrix factorization","volume":"52","author":"Huang","year":"2014","journal-title":"IEEE Trans Geosci. Remote Sens."},{"key":"ref_20","first-page":"1779","article-title":"Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic","volume":"30","author":"Chavez","year":"1991","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","unstructured":"Haydn, R., Dalke, G., Henkel, J., and Bare, J. (1982, January 19\u201325). Application of the IHS color transform to the processing of multisensor data and image enhancement. Proceedings of the International Symposium on Remote Sensing of Environment, First Thematic Conference: Remote Sensing of Arid and Semi-Arid Lands, Cairo, Egypt."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/LGRS.2008.2012003","article-title":"A Comparison between global and context-adaptive pansharpening of multispectral images","volume":"6","author":"Aiazzi","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/LGRS.2005.861699","article-title":"Spatial resolution improvement by merging MERIS-ETM images for coastal water monitoring","volume":"3","author":"Polidori","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/LGRS.2008.919685","article-title":"Unmixing-based landsat TM ane MERIS FR data fusion","volume":"5","author":"Clevers","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Masi, G., Cozzolino, D., Verdoliva, L., and Scarpa, G. (2016). Pansharpening by convolutional neural networks. Remote Sens., 8.","DOI":"10.3390\/rs8070594"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1080\/01431160802639525","article-title":"Merfing hyperspectral and panchromatic image data: Qualitative and quantitative analysis","volume":"30","author":"Cetin","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kawakami, R., Wright, J., Tai, Y.W., Matsushita, Y., Ben-Ezra, M., and Ikeuchi, K. (2011, January 20\u201325). High-resolution hyperspectral imaging via matrix factorization. Proceedings of the CVPR, Providence, RI, USA.","DOI":"10.1109\/CVPR.2011.5995457"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled nonnegative matrix factorization for hyperspectral and multispectral data fusion","volume":"50","author":"Yokoya","year":"2012","journal-title":"IEEE Trans Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Lee, D.D., and Seung, S.H. (2001). Algorithms for non-negative matrix factorization. NIPS, 556\u2013562."},{"key":"ref_30","unstructured":"Liu, D., Wen, B., Fan, Y., Loy, C., and Huang, T. (2018, January 3\u20138). Non-Local Recurrent Network for Image Restoration. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_31","unstructured":"Andrea, B., Diego, V., Giulia, F., and Enrico, M. (2019). DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TIP.2016.2542360","article-title":"Hyperspectral Image Super-Resolution via Non-Negative Strutured Sparse Representation","volume":"25","author":"Dong","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: Geometrical, statistical and sparse regression-based approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.sigpro.2005.05.030","article-title":"Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit","volume":"86","author":"Tropp","year":"2006","journal-title":"Signal Process."},{"key":"ref_35","unstructured":"Yu, K., Zhang, T., and Gong, Y. (2009). Nonlinear learning using local coordinate coding. NIPS, 2223\u20132231."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, J., Yu, K., Huang, F.L.T., and Gong, Y. (2010, January 13\u201318). Locality-constrained linear coding for image classification. Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540018"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TIP.2010.2046811","article-title":"Generalized assorted pixel camera: Post-capture control of resolution, dynamic range and spectrum","volume":"19","author":"Yasuma","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chakrabarti, A., and Zickler, T. (2011, January 20\u201325). Statistics of real-world hyperspectral images. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995660"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MGRS.2016.2637824","article-title":"Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature","volume":"5","author":"Yokoya","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2018.02.105","article-title":"Hyperspectral image classification using spectral-spatial LSTMs","volume":"328","author":"Zhou","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ahmad, M., Khan, A., Khan, A.M., Mazzara, M., Distefano, S., Sohaib, A., and Nibouche, O. (2019). Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images. Remote Sens., 11.","DOI":"10.3390\/rs11091136"},{"key":"ref_43","unstructured":"Wald, L. Quality of high resolution synthesisted images: Is there a simple criterion?. Available online: https:\/\/hal.archives-ouvertes.fr\/hal-00395027\/."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wcoff, E., Chan, T., Jia, K., Ma, W., and Ma, Y. (2013, January 26\u201331). A non-negative sparse promoting algorithm for high resolution hyperspectral imaging. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6637883"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5401\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:40:21Z","timestamp":1760190021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5401"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,7]]},"references-count":44,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19245401"],"URL":"https:\/\/doi.org\/10.3390\/s19245401","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,12,7]]}}}