{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:44:02Z","timestamp":1762508642850,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030891305"},{"type":"electronic","value":"9783030891312"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89131-2_3","type":"book-chapter","created":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T06:18:05Z","timestamp":1635574685000},"page":"24-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Land Use Change Detection Using Deep Siamese Neural Networks and Weakly Supervised Learning"],"prefix":"10.1007","author":[{"given":"Indrajit","family":"Kalita","sequence":"first","affiliation":[]},{"given":"Savvas","family":"Karatsiolis","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Kamilaris","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Andermatt, P., Timofte, R.: A weakly supervised convolutional network for change segmentation and classification. arXiv preprint arXiv:2011.03577 (2020)","DOI":"10.1007\/978-3-030-69756-3_8"},{"issue":"10","key":"3_CR2","doi-asserted-by":"publisher","first-page":"3416","DOI":"10.1109\/TGRS.2009.2022633","volume":"47","author":"C Benedek","year":"2009","unstructured":"Benedek, C., Szir\u00e1nyi, T.: Change detection in optical aerial images by a multilayer conditional mixed Markov model. IEEE Trans. Geosci. Remote Sens. 47(10), 3416\u20133430 (2009)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Bourdis, N., Marraud, D., Sahbi, H.: Constrained optical flow for aerial image change detection. In: International Geoscience and Remote Sensing Symposium, pp. 4176\u20134179. IEEE (2011)","DOI":"10.1109\/IGARSS.2011.6050150"},{"issue":"7","key":"3_CR4","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1109\/TGRS.2008.916643","volume":"46","author":"F Bovolo","year":"2008","unstructured":"Bovolo, F., Bruzzone, L., Marconcini, M.: A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Trans. Geosci. Remote Sens. 46(7), 2070\u20132082 (2008)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"3_CR5","doi-asserted-by":"publisher","first-page":"2403","DOI":"10.1109\/TGRS.2009.2038274","volume":"48","author":"D Brunner","year":"2010","unstructured":"Brunner, D., Lemoine, G., Bruzzone, L.: Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Trans. Geosci. Remote Sens. 48(5), 2403\u20132420 (2010)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"23","key":"3_CR6","doi-asserted-by":"publisher","first-page":"7161","DOI":"10.1080\/01431161.2017.1371861","volume":"38","author":"G Cao","year":"2017","unstructured":"Cao, G., Wang, B., Xavier, H., Yang, D., Southworth, J.: A new difference image creation method based on deep neural networks for change detection in remote-sensing images. Int. J. Remote Sens. 38(23), 7161\u20137175 (2017)","journal-title":"Int. J. Remote Sens."},{"issue":"4","key":"3_CR7","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","volume":"6","author":"T Celik","year":"2009","unstructured":"Celik, T.: Unsupervised change detection in satellite images using principal component analysis and $$k$$-means clustering. IEEE Geosci. Remote Sens. Lett. 6(4), 772\u2013776 (2009)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 1735\u20131742. IEEE (2006)","DOI":"10.1109\/CVPR.2006.100"},{"issue":"6","key":"3_CR9","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1109\/LSP.2018.2809688","volume":"25","author":"J Hu","year":"2018","unstructured":"Hu, J., Chen, Z., Yang, M., Zhang, R., Cui, Y.: A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Process. Lett. 25(6), 853\u2013857 (2018)","journal-title":"IEEE Signal Process. Lett."},{"issue":"11","key":"3_CR10","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.3390\/rs11111343","volume":"11","author":"S Ji","year":"2019","unstructured":"Ji, S., Shen, Y., Lu, M., Zhang, Y.: Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples. Remote Sens. 11(11), 1343 (2019)","journal-title":"Remote Sens."},{"issue":"1","key":"3_CR11","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","volume":"57","author":"S Ji","year":"2018","unstructured":"Ji, S., Wei, S., Lu, M.: Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Trans. Geosci. Remote Sens. 57(1), 574\u2013586 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"de Jong, K.L., Bosman, A.S.: Unsupervised change detection in satellite images using convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8851762"},{"key":"3_CR13","doi-asserted-by":"publisher","unstructured":"Kalita, I., Roy, M.: Deep neural network-based heterogeneous domain adaptation using ensemble decision making in land cover classification. IEEE Trans. Artif. Intell. (2020). https:\/\/doi.org\/10.1109\/TAI.2020.3043724","DOI":"10.1109\/TAI.2020.3043724"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Khan, S.H., He, X., Porikli, F., Bennamoun, M., Sohel, F., Togneri, R.: Learning deep structured network for weakly supervised change detection. arXiv preprint arXiv:1606.02009 (2016)","DOI":"10.24963\/ijcai.2017\/279"},{"key":"3_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"1","key":"3_CR16","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/LGRS.2019.2916601","volume":"17","author":"J Liu","year":"2019","unstructured":"Liu, J., et al.: Convolutional neural network-based transfer learning for optical aerial images change detection. IEEE Geosci. Remote Sens. Lett. 17(1), 127\u2013131 (2019)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"3","key":"3_CR17","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TNNLS.2016.2636227","volume":"29","author":"J Liu","year":"2016","unstructured":"Liu, J., Gong, M., Qin, K., Zhang, P.: A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 545\u2013559 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"3_CR18","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/JSTARS.2015.2403297","volume":"8","author":"SK Meher","year":"2015","unstructured":"Meher, S.K., Kumar, D.A.: Ensemble of adaptive rule-based granular neural network classifiers for multispectral remote sensing images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2222\u20132231 (2015)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Mubea, K., Menz, G.: Monitoring land-use change in Nakuru (Kenya) using multi-sensor satellite data (2012)","DOI":"10.4236\/ars.2012.13008"},{"issue":"1","key":"3_CR20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man and Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man and Cybern."},{"issue":"10","key":"3_CR21","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","volume":"14","author":"Y Zhan","year":"2017","unstructured":"Zhan, Y., Fu, K., Yan, M., Sun, X., Wang, H., Qiu, X.: Change detection based on deep Siamese convolutional network for optical aerial images. IEEE Geosci. Remote Sens. Lett. 14(10), 1845\u20131849 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"12","key":"3_CR22","doi-asserted-by":"publisher","first-page":"3978","DOI":"10.1109\/TGRS.2007.907109","volume":"45","author":"P Zhong","year":"2007","unstructured":"Zhong, P., Wang, R.: A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images. IEEE Trans. Geosci. Remote Sens. 45(12), 3978\u20133988 (2007)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Computer Analysis of Images and Patterns"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89131-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T03:21:04Z","timestamp":1726024864000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89131-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030891305","9783030891312"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89131-2_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":"31 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Analysis of Images and Patterns","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cyprusconferences.org\/caip2021\/","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":"EasyAcademia","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"129","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":"87","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":"67% - 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":"2","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":"4","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)"}}]}}