{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:33:47Z","timestamp":1742913227739,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030617240"},{"type":"electronic","value":"9783030617257"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-61725-7_20","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T13:05:25Z","timestamp":1604322325000},"page":"163-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Uncertainty Estimation for Semantic Segmentation of Hyperspectral Imagery"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0079-9495","authenticated-orcid":false,"given":"Aneesh","family":"Rangnekar","sequence":"first","affiliation":[]},{"given":"Emmett","family":"Ientilucci","sequence":"additional","affiliation":[]},{"given":"Christopher","family":"Kanan","sequence":"additional","affiliation":[]},{"given":"Matthew J.","family":"Hoffman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"20_CR1","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561 (2015)"},{"key":"20_CR2","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424 (2015)"},{"issue":"1","key":"20_CR3","doi-asserted-by":"publisher","first-page":"33","DOI":"10.3847\/1538-3881\/ab2390","volume":"158","author":"AD Cobb","year":"2019","unstructured":"Cobb, A.D., et al.: An ensemble of bayesian neural networks for exoplanetary atmospheric retrieval. Astronomical J. 158(1), 33 (2019)","journal-title":"Astronomical J."},{"key":"20_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1007\/978-3-540-24688-6_86","volume-title":"Computational Science - ICCS 2004","author":"F Darema","year":"2004","unstructured":"Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662\u2013669. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-24688-6_86"},{"issue":"2","key":"20_CR5","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.strusafe.2008.06.020","volume":"31","author":"A Der Kiureghian","year":"2009","unstructured":"Der Kiureghian, A., Ditlevsen, O.: Aleatory or epistemic? does it matter? Structural Safety 31(2), 105\u2013112 (2009)","journal-title":"Structural Safety"},{"issue":"1","key":"20_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-018-07882-8","volume":"10","author":"S Fletcher","year":"2019","unstructured":"Fletcher, S., Lickley, M., Strzepek, K.: Learning about climate change uncertainty enables flexible water infrastructure planning. Nat. Commun. 10(1), 1\u201311 (2019)","journal-title":"Nat. Commun."},{"key":"20_CR7","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international Conference on Machine Learning, pp. 1050\u20131059 (2016)"},{"key":"20_CR8","unstructured":"Huang, G., Li, Y., Pleiss, G., Liu, Z., Hopcroft, J.E., Weinberger, K.Q.: Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109 (2017)"},{"key":"20_CR9","unstructured":"Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680 (2015)"},{"key":"20_CR10","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574\u20135584 (2017)"},{"key":"20_CR11","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, pp. 6402\u20136413 (2017)"},{"key":"20_CR12","unstructured":"Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"20_CR13","first-page":"1","volume":"1","author":"A Rangnekar","year":"2020","unstructured":"Rangnekar, A., Mokashi, N., Ientilucci, E.J., Kanan, C., Hoffman, M.J.: Aerorit: a new scene for hyperspectral image analysis. IEEE Trans. Geosci. Remote Sens. 1, 1\u20139 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"20_CR14","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1016\/j.procs.2014.05.113","volume":"29","author":"V Rao","year":"2014","unstructured":"Rao, V., Sandu, A.: A posteriori error estimates for dddas inference problems. Procedia Comput. Sci. 29, 1256\u20131265 (2014)","journal-title":"Procedia Comput. Sci."},{"key":"20_CR15","unstructured":"Ritter, H., Botev, A., Barber, D.: A scalable laplace approximation for neural networks. In: 6th International Conference on Learning Representations, ICLR 2018-Conference Track Proceedings. vol. 6. International Conference on Representation Learning (2018)"},{"key":"20_CR16","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"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Uzkent, B., Rangnekar, A., Hoffman, M.J.: Aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 233\u2013242. IEEE (2017)","DOI":"10.1109\/CVPRW.2017.35"},{"issue":"1","key":"20_CR18","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1109\/TGRS.2018.2856370","volume":"57","author":"B Uzkent","year":"2018","unstructured":"Uzkent, B., Rangnekar, A., Hoffman, M.J.: Tracking in aerial hyperspectral videos using deep kernelized correlation filters. IEEE Trans. Geosci. Remote Sens. 57(1), 449\u2013461 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"20_CR19","unstructured":"Wen, Y., Tran, D., Ba, J.: Batchensemble: An alternative approach to efficient ensemble and lifelong learning (2020)"}],"container-title":["Lecture Notes in Computer Science","Dynamic Data Driven Applications Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61725-7_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T20:05:28Z","timestamp":1608840328000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61725-7_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030617240","9783030617257"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61725-7_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DDDAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Dynamic Data Driven Application Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Boston, MA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"2 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dddas2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/dddas-conf\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","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":"21","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":"14","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":"53% - 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":"5","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":"10","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)"}}]}}