{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T20:18:22Z","timestamp":1758399502285,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030697556"},{"type":"electronic","value":"9783030697563"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69756-3_3","type":"book-chapter","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T08:03:17Z","timestamp":1614067397000},"page":"31-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Unsupervised Multispectral and Hyperspectral Image Fusion with Deep Spatial and Spectral Priors"],"prefix":"10.1007","author":[{"given":"Zhe","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yinqiang","family":"Zheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5003-3180","authenticated-orcid":false,"given":"Xian-Hua","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,24]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","volume":"113","author":"A Plaza","year":"2009","unstructured":"Plaza, A., et al.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113, S110\u2013S122 (2009)","journal-title":"Remote Sens. Environ."},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","volume":"113","author":"AF Goetz","year":"2009","unstructured":"Goetz, A.F.: Three decades of hyperspectral remote sensing of the earth: a personal view. Remote Sens. Environ. 113, S5\u2013S16 (2009)","journal-title":"Remote Sens. Environ."},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","volume":"19","author":"G Lu","year":"2014","unstructured":"Lu, G., Fei, B.: Medical hyperspectral imaging: a review. J. Biomed. Opt. 19, 010901 (2014)","journal-title":"J. Biomed. Opt."},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/79.974724","volume":"19","author":"D Manolakis","year":"2002","unstructured":"Manolakis, D., Shaw, G.: Detection algorithms for hyperspectral imaging applications. IEEE Sig. Process. Mag. 19, 29\u201343 (2002)","journal-title":"IEEE Sig. Process. Mag."},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., Doulamis, N.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959\u20134962. IEEE (2015)","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"3_CR6","first-page":"79","volume":"14","author":"D Manolakis","year":"2003","unstructured":"Manolakis, D., Marden, D., Shaw, G.A., et al.: Hyperspectral image processing for automatic target detection applications. Lincoln Lab. J. 14, 79\u2013116 (2003)","journal-title":"Lincoln Lab. J."},{"key":"3_CR7","unstructured":"Treado, P., Nelson, M., Gardner Jr., C.: Hyperspectral imaging sensor for tracking moving targets (2012). US Patent App. 13\/199,981"},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"3574","DOI":"10.1109\/TIP.2014.2329767","volume":"23","author":"MA Veganzones","year":"2014","unstructured":"Veganzones, M.A., Tochon, G., Dalla-Mura, M., Plaza, A.J., Chanussot, J.: Hyperspectral image segmentation using a new spectral unmixing-based binary partition tree representation. IEEE Trans. Image Process. 23, 3574\u20133589 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","volume":"49","author":"Y Chen","year":"2011","unstructured":"Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49, 3973\u20133985 (2011)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3_CR10","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1109\/TGRS.2014.2321557","volume":"53","author":"YQ Zhao","year":"2014","unstructured":"Zhao, Y.Q., Yang, J.: Hyperspectral image denoising via sparse representation and low-rank constraint. IEEE Trans. Geosci. Remote Sens. 53, 296\u2013308 (2014)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"7008","DOI":"10.1109\/TGRS.2013.2248013","volume":"52","author":"H Pu","year":"2014","unstructured":"Pu, H., Chen, Z., Wang, B., Jiang, G.M.: A novel spatial-spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 52, 7008\u20137022 (2014)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.3390\/s18061695","volume":"18","author":"H Yu","year":"2018","unstructured":"Yu, H., Gao, L., Liao, W., Zhang, B.: Group sparse representation based on nonlocal spatial and local spectral similarity for hyperspectral imagery classification. Sensors 18, 1695 (2018)","journal-title":"Sensors"},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295\u2013307 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Yokoya, N., Yairi, T., Iwasaki, A.: Coupled non-negative matrix factorization (CNMF) for hyperspectral and multispectral data fusion: application to pasture classification. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 1779\u20131782. IEEE (2011)","DOI":"10.1109\/IGARSS.2011.6049465"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Lanaras, C., Baltsavias, E., Schindler, K.: Hyperspectral super-resolution by coupled spectral unmixing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3586\u20133594 (2015)","DOI":"10.1109\/ICCV.2015.409"},{"key":"3_CR16","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)"},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1109\/TGRS.2012.2213604","volume":"51","author":"XX Zhu","year":"2012","unstructured":"Zhu, X.X., Bamler, R.: A sparse image fusion algorithm with application to pan-sharpening. IEEE Trans. Geosci. Remote Sens. 51, 2827\u20132836 (2012)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TGRS.2010.2051674","volume":"49","author":"J Choi","year":"2010","unstructured":"Choi, J., Yu, K., Kim, Y.: A new adaptive component-substitution-based satellite image fusion by using partial replacement. IEEE Trans. Geosci. Remote Sens. 49, 295\u2013309 (2010)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3_CR19","first-page":"447","volume":"2","author":"A Dhore","year":"2014","unstructured":"Dhore, A., Veena, C.: A new pan-sharpening method using joint sparse FI image fusion algorithm. Int. J. Eng. Res. General Sci. 2, 447\u201355 (2014)","journal-title":"Int. J. Eng. Res. General Sci."},{"key":"3_CR20","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3390\/rs8020099","volume":"8","author":"H Liang","year":"2016","unstructured":"Liang, H., Li, Q.: Hyperspectral imagery classification using sparse representations of convolutional neural network features. Remote Sens. 8, 99 (2016)","journal-title":"Remote Sens."},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TGRS.2015.2504261","volume":"54","author":"XX Zhu","year":"2015","unstructured":"Zhu, X.X., Grohnfeldt, C., Bamler, R.: Exploiting joint sparsity for pansharpening: The j-sparsefi algorithm. IEEE Trans. Geosci. Remote Sens. 54, 2664\u20132681 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-319-10584-0_5","volume-title":"Computer Vision \u2013 ECCV 2014","author":"N Akhtar","year":"2014","unstructured":"Akhtar, N., Shafait, F., Mian, A.: Sparse spatio-spectral representation for hyperspectral image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 63\u201378. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10584-0_5"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Meng, G., Li, G., Dong, W., Shi, G.: Non-negative structural sparse representation for high-resolution hyperspectral imaging. In: Optoelectronic Imaging and Multimedia Technology III, vol. 9273. International Society for Optics and Photonics (2014). 92730H","DOI":"10.1117\/12.2071661"},{"key":"3_CR24","doi-asserted-by":"publisher","first-page":"5625","DOI":"10.1109\/TIP.2018.2855418","volume":"27","author":"XH Han","year":"2018","unstructured":"Han, X.H., Shi, B., Zheng, Y.: Self-similarity constrained sparse representation for hyperspectral image super-resolution. IEEE Trans. Image Process. 27, 5625\u20135637 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Han, X.H., Chen, Y.W.: Deep residual network of spectral and spatial fusion for hyperspectral image super-resolution. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 266\u2013270. IEEE (2019)","DOI":"10.1109\/BigMM.2019.00-13"},{"key":"3_CR26","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1109\/LGRS.2017.2668299","volume":"14","author":"F Palsson","year":"2017","unstructured":"Palsson, F., Sveinsson, J.R., Ulfarsson, M.O.: Multispectral and hyperspectral image fusion using a 3-D-convolutional neural network. IEEE Geosci. Remote Sens. Lett. 14, 639\u2013643 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Dian, R., Li, S., Guo, A., Fang, L.: Deep hyperspectral image sharpening. IEEE Trans. Neural Netw. Learn. Syst. 1\u201311 (2018)","DOI":"10.1109\/TNNLS.2018.2798162"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Qu, Y., Qi, H., Kwan, C.: Unsupervised sparse Dirichlet-net for hyperspectral image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2511\u20132520 (2018)","DOI":"10.1109\/CVPR.2018.00266"},{"key":"3_CR29","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Sidorov, O., Hardeberg, J.Y.: Deep hyperspectral prior: Denoising, inpainting, super-resolution. CoRR abs\/1902.00301 (2019)","DOI":"10.1109\/ICCVW.2019.00477"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Kawakami, R., Matsushita, Y., Wright, J., Ben-Ezra, M., Tai, Y.W., Ikeuchi, K.: High-resolution hyperspectral imaging via matrix factorization. In: CVPR 2011, pp. 2329\u20132336. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995457"},{"key":"3_CR32","doi-asserted-by":"publisher","first-page":"3658","DOI":"10.1109\/TGRS.2014.2381272","volume":"53","author":"Q Wei","year":"2015","unstructured":"Wei, Q., Bioucas-Dias, J., Dobigeon, N., Tourneret, J.Y.: Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Trans. Geosci. Remote Sens. 53, 3658\u20133668 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69756-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T08:07:41Z","timestamp":1614067661000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69756-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030697556","9783030697563"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69756-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"24 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"254","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}