{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:40:20Z","timestamp":1742913620734,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031314377"},{"type":"electronic","value":"9783031314384"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-31438-4_13","type":"book-chapter","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T08:02:53Z","timestamp":1682496173000},"page":"191-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Analysis of\u00a0Spatial-Spectral Dependence in\u00a0Hyperspectral Autoencoders"],"prefix":"10.1007","author":[{"given":"William Michael","family":"Laprade","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesper Cairo","family":"Westergaard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jon","family":"Nielsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mads","family":"Nielsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anders Bjorholm","family":"Dahl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Ad\u00e3o, T., et al.: Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 9(11), 1110 (2017). https:\/\/doi.org\/10.3390\/rs9111110,https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1110","DOI":"10.3390\/rs9111110,"},{"issue":"2","key":"13_CR2","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","volume":"7","author":"N Audebert","year":"2019","unstructured":"Audebert, N., Le Saux, B., Lefevre, S.: Deep learning for classification of hyperspectral data: a comparative review. IEEE Geosci. Remote Sens. Mag. 7(2), 159\u2013173 (2019). https:\/\/doi.org\/10.1109\/MGRS.2019.2912563","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Barberio, M., et al.: Intraoperative guidance using hyperspectral imaging: a review for surgeons. Diagnostics 11(11), 2066 (2021). https:\/\/doi.org\/10.3390\/diagnostics11112066","DOI":"10.3390\/diagnostics11112066"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Bock, C.H., Poole, G.H., Parker, P.E., Gottwald, T.R.: Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit. Rev. Plant Sci. 29(2), 59\u2013107 (2010). https:\/\/doi.org\/10.1080\/07352681003617285","DOI":"10.1080\/07352681003617285"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Gitelson, A.A., Gritz $$\\dagger $$, Y., Merzlyak, M.N.: Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160(3), 271\u2013282 (2003). https:\/\/doi.org\/10.1078\/0176-1617-00887,https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0176161704704034","DOI":"10.1078\/0176-1617-00887,"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Gowen, A., O\u2019Donnell, C., Cullen, P., Downey, G., Frias, J.: Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18(12), 590\u2013598 (2007). https:\/\/doi.org\/10.1016\/j.tifs.2007.06.001,https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0924224407002026","DOI":"10.1016\/j.tifs.2007.06.001,"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Hege, E.K., O\u2019Connell, D., Johnson, W., Basty, S., Dereniak, E.L.: Hyperspectral imaging for astronomy and space surveillance. In: Shen, S.S., Lewis, P.E. (eds.) Imaging Spectrometry IX. vol. 5159, pp. 380\u2013391. International Society for Optics and Photonics, SPIE (2004). https:\/\/doi.org\/10.1117\/12.506426","DOI":"10.1117\/12.506426"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Huang, S.Y., Mukundan, A., Yu-Ming, T., Kim, Y., Lin, F.C., Wang, H.C.: Recent advances in counterfeit art, document, photo, hologram, and currency detection using hyperspectral imaging. Sensors 22, 7308 (2022). https:\/\/doi.org\/10.3390\/s22197308","DOI":"10.3390\/s22197308"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Ji, S., Zhang, C., Xu, A., Shi, Y., Duan, Y.: 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens. 10(2), 75 (2018). https:\/\/doi.org\/10.3390\/rs10010075","DOI":"10.3390\/rs10010075"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Lowe, A., Harrison, N., French, A.P.: Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13(1), 80 (2017). https:\/\/doi.org\/10.1186\/s13007-017-0233-z","DOI":"10.1186\/s13007-017-0233-z"},{"issue":"16","key":"13_CR11","doi-asserted-by":"publisher","first-page":"2659","DOI":"10.3390\/rs12162659","volume":"12","author":"B Lu","year":"2020","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., Shang, J.: Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens. 12(16), 2659 (2020)","journal-title":"Remote Sens."},{"issue":"10","key":"13_CR12","doi-asserted-by":"publisher","DOI":"10.1117\/1.jbo.19.10.106004","volume":"19","author":"G Lu","year":"2014","unstructured":"Lu, G., Halig, L., Wang, D., Qin, X., Chen, Z.G., Fei, B.: Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging. J. Biomed. Opt. 19(10), 106004 (2014). https:\/\/doi.org\/10.1117\/1.jbo.19.10.106004","journal-title":"J. Biomed. Opt."},{"key":"13_CR13","doi-asserted-by":"publisher","unstructured":"Nagasubramanian, K., Jones, S., Singh, A.K., Sarkar, S., Singh, A., Ganapathysubramanian, B.: Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15(1), 1\u201310 (2019). https:\/\/doi.org\/10.1186\/s13007-019-0479-8","DOI":"10.1186\/s13007-019-0479-8"},{"key":"13_CR14","doi-asserted-by":"publisher","unstructured":"Osco, L.P., et al.: A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements. Remote Sens. 12(6), 906 (2020). https:\/\/doi.org\/10.3390\/rs12060906,https:\/\/www.mdpi.com\/2072-4292\/12\/6\/906","DOI":"10.3390\/rs12060906,"},{"key":"13_CR15","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","volume":"158","author":"M Paoletti","year":"2019","unstructured":"Paoletti, M., Haut, J., Plaza, J., Plaza, A.: Deep learning classifiers for hyperspectral imaging: a review. ISPRS J. Photogrammetry Remote Sens. 158, 279\u2013317 (2019)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"13_CR16","doi-asserted-by":"publisher","unstructured":"Peng, X., et al.: Prediction of the nitrogen, phosphorus and potassium contents in grape leaves at different growth stages based on UAV multispectral remote sensing. Remote Sens. 14(11), 2659 (2022). https:\/\/doi.org\/10.3390\/rs14112659,https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2659","DOI":"10.3390\/rs14112659,"},{"key":"13_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"7","key":"13_CR18","doi-asserted-by":"publisher","first-page":"5205","DOI":"10.1007\/s10462-021-10018-y","volume":"54","author":"C Wang","year":"2021","unstructured":"Wang, C., et al.: A review of deep learning used in the hyperspectral image analysis for agriculture. Artif. Intell. Rev. 54(7), 5205\u20135253 (2021). https:\/\/doi.org\/10.1007\/s10462-021-10018-y","journal-title":"Artif. Intell. Rev."}],"container-title":["Lecture Notes in Computer Science","Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31438-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T09:04:37Z","timestamp":1685351077000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31438-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031314377","9783031314384"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31438-4_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SCIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Scandinavian Conference on Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lapland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/scia2023\/","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":"CMT 3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"108","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":"67","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":"62% - 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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}