{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:20:08Z","timestamp":1743096008123,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811912528"},{"type":"electronic","value":"9789811912535"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-981-19-1253-5_19","type":"book-chapter","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T17:03:29Z","timestamp":1648055009000},"page":"258-270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Object Relations Focused Siamese Network for Remote Sensing Image Change Detection"],"prefix":"10.1007","author":[{"given":"Jie-pei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei-yu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian-cong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guo-qiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"issue":"12","key":"19_CR1","doi-asserted-by":"publisher","first-page":"7077","DOI":"10.1109\/TGRS.2016.2594952","volume":"54","author":"M Gong","year":"2016","unstructured":"Gong, M., Zhang, P., Su, L., Liu, J.: Coupled dictionary learning for change detection from multi-source data. IEEE Trans. Geosci. Remote Sens. 54(12), 7077\u20137091 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.rse.2013.01.012","volume":"132","author":"S Jin","year":"2011","unstructured":"Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., Xian, G.: A comprehensive change detection method for updating the national land cover database to circa. Remote Sens. Environ. 132, 159\u2013175 (2011)","journal-title":"Remote Sens. Environ."},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Chen, H., Qi, Z., Shi Z.: Efficient Transformer based Method for Remote Sensing Image Change Detection (2021)","DOI":"10.1109\/TGRS.2021.3095166"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Liu, Y., Pang, C., Zhan, Z., et al.: Building change detection for remote sensing images using a dual-task constrained deep Siamese convolutional network model. IEEE Geosci. Remote Sens. Lett. 18, 811\u2013815 (2021)","DOI":"10.1109\/LGRS.2020.2988032"},{"issue":"4","key":"19_CR5","doi-asserted-by":"publisher","first-page":"2720","DOI":"10.1109\/TGRS.2019.2953879","volume":"58","author":"W Zhao","year":"2020","unstructured":"Zhao, W., Mou, L., Chen, J., Bo, Y., Emery, W.J.: Incorporating metric learning and adversarial network for seasonal invariant change detection. IEEE Trans. Geosci. Remote. Sens 58(4), 2720\u20132731 (2020)","journal-title":"IEEE Trans. Geosci. Remote. Sens"},{"issue":"3","key":"19_CR6","doi-asserted-by":"publisher","first-page":"484","DOI":"10.3390\/rs12030484","volume":"12","author":"H Jiang","year":"2020","unstructured":"Jiang, H., Hu, X., Li, K., Zhang, J., et al.: Pga-siamnet: pyramid feature-based attention-guided siamese network for remote sensing orthoimagery building change detection. Remote Sens. 12(3), 484 (2020)","journal-title":"Remote Sens."},{"issue":"4","key":"19_CR7","first-page":"640","volume":"39","author":"J Long","year":"2015","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. Med. Image Comput. Comput. Assist. Intervent. MICCAI 9351, 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"19_CR9","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/LGRS.2018.2869608","volume":"16","author":"M Zhang","year":"2019","unstructured":"Zhang, M., Xu, G., Chen, K., Yan, M., Sun, X.: Tripletbased semantic relation learning for aerial remote sensing image change detection. IEEE Geosci. Remote. Sens. Lett. 16(2), 266\u2013270 (2019)","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6230\u20136239 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"19_CR11","unstructured":"Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Keysers, D., Uszkoreit, J., Lucic, M., et al.: MLP-Mixer: An all-MLP Architecture for Vision. arXiv preprint arXiv:2105.01601 (2021)"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhong, Y., Wang, J., Ma, A.: Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4095\u20134104 (2020)","DOI":"10.1109\/CVPR42600.2020.00415"},{"issue":"2","key":"19_CR13","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1109\/TGRS.2005.861007","volume":"44","author":"C Carincotte","year":"2006","unstructured":"Carincotte, C., Derrode, S., Bourennane, S.: Unsupervised change detection on SAR images using fuzzy hidden Markov chains. IEEE Trans. Geosci. Remote Sens. 44(2), 432\u2013441 (2006). https:\/\/doi.org\/10.1109\/TGRS.2005.861007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"19_CR14","doi-asserted-by":"publisher","first-page":"4645","DOI":"10.1007\/s00500-014-1460-0","volume":"20","author":"J Liu","year":"2014","unstructured":"Liu, J., Gong, M., Zhao, J., Li, H., Jiao, L.: Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images. Soft. Comput. 20(12), 4645\u20134657 (2014). https:\/\/doi.org\/10.1007\/s00500-014-1460-0","journal-title":"Soft. Comput."},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Kayikcioglu, I., Kose, C., Kayikcioglu T.: ECG ST segment change detection using Born-Jordan time-frequency transform and artificial neural networks. In: Signal Processing and Communications Applications Conference (SIU) (2018)","DOI":"10.1109\/SIU.2018.8404266"},{"issue":"18","key":"19_CR16","doi-asserted-by":"publisher","first-page":"5325","DOI":"10.1007\/s00500-016-2116-z","volume":"21","author":"H Wang","year":"2016","unstructured":"Wang, H., Cui, Z., Sun, H., Rahnamayan, S., Yang, X.-S.: Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft. Comput. 21(18), 5325\u20135339 (2016). https:\/\/doi.org\/10.1007\/s00500-016-2116-z","journal-title":"Soft. Comput."},{"issue":"7","key":"19_CR17","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1007\/s00521-015-1998-5","volume":"31","author":"J Fan","year":"2015","unstructured":"Fan, J.: OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm. Neural Comput. Appl. 31(7), 2095\u20132105 (2015). https:\/\/doi.org\/10.1007\/s00521-015-1998-5","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"19_CR18","doi-asserted-by":"publisher","first-page":"e6078","DOI":"10.1002\/cpe.6078","volume":"33","author":"L Tang","year":"2021","unstructured":"Tang, L., Wang, C., Wang, S., et al.: A novel fuzzy clustering algorithm based on rough set and inhibitive factor. Concurr. Comput. Pract. Exper. 33(6), e6078 (2021)","journal-title":"Concurr. Comput. Pract. Exper."},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Li, W., Lu, M., Chen, X.: Automatic change detection of urban land-cover based on SVM classification. In: Geoscience & Remote Sensing Symposium. IEEE 1686\u20131689 (2015)","DOI":"10.1109\/IGARSS.2015.7326111"},{"key":"19_CR20","unstructured":"Daudt, R.C., Saux, B.L., Boulch, A.: Fully convolutional siamese networks for change detection. In: ICIP (2018)"},{"key":"19_CR21","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","volume":"166","author":"A Cz","year":"2020","unstructured":"Cz, A., Peng, Y., Dt, E., et al.: A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS J. Photogramm. Remote. Sens. 166, 183\u2013200 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"19_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3056416","volume":"19","author":"S Fang","year":"2022","unstructured":"Fang, S., Li, K., Shao, J., Li, Z.: SNUNet-CD: a densely connected Siamese network for change detection of VHR Images. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022). https:\/\/doi.org\/10.1109\/LGRS.2021.3056416","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"20","key":"19_CR23","doi-asserted-by":"publisher","first-page":"15821","DOI":"10.1007\/s00500-020-04912-w","volume":"24","author":"M Lopez-Pacheco","year":"2020","unstructured":"Lopez-Pacheco, M., Morales-Valdez, J., Wen, Y.: Frequency domain CNN and dissipated energy approach for damage detection in building structures. Soft Comput. 24(20), 15821\u201315840 (2020). https:\/\/doi.org\/10.1007\/s00500-020-04912-w","journal-title":"Soft Comput."},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Papandreou, G., et al.: Rethinking atrous convolution for semantic image segmentation. arXiv (2017)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"19_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/978-3-030-01252-6_24","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Liu","year":"2018","unstructured":"Liu, S., Huang, D., Wang, Y.: Receptive field block net for accurate and fast object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 404\u2013419. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_24"},{"issue":"10","key":"19_CR26","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.3390\/rs12101662","volume":"12","author":"H Chen","year":"2020","unstructured":"Chen, H., Shi, Z.: A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens. 12(10), 1662 (2020). https:\/\/doi.org\/10.3390\/rs12101662","journal-title":"Remote Sens."},{"key":"19_CR27","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","volume":"14","author":"J Chen","year":"2021","unstructured":"Chen, J., Yuan, Z., Peng, J., Chen, L., Huang, H., Jiawei Zhu, Y., Liu, H.L.: DASNet: dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14, 1194\u20131206 (2021). https:\/\/doi.org\/10.1109\/JSTARS.2020.3037893","journal-title":"IEEE J. Select. Top. Appl. Earth Observ. Remote Sens."},{"issue":"18","key":"19_CR28","doi-asserted-by":"publisher","first-page":"3707","DOI":"10.3390\/rs13183707","volume":"13","author":"FI Diakogiannis","year":"2021","unstructured":"Diakogiannis, F.I., Waldner, F., Caccetta, P.: Looking for change? roll the dice and demand attention. Remote Sens. 13(18), 3707 (2021). https:\/\/doi.org\/10.3390\/rs13183707","journal-title":"Remote Sens."},{"key":"19_CR29","doi-asserted-by":"publisher","first-page":"565","DOI":"10.5194\/isprs-archives-XLII-2-565-2018","volume":"XLII-2","author":"MA Lebedev","year":"2018","unstructured":"Lebedev, M.A., Vizilter, Y.V., Vygolov, O.V., Knyaz, V.A., Rubis, A.Y.: Change detection in remote sensing images using conditional adversarial networks. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLII\u20132, 565\u2013571 (2018). https:\/\/doi.org\/10.5194\/isprs-archives-XLII-2-565-2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"19_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: A nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Communications in Computer and Information Science","Bio-Inspired Computing: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-1253-5_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T21:55:47Z","timestamp":1726869347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-1253-5_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811912528","9789811912535"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-1253-5_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIC-TA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bio-Inspired Computing: Theories and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiyuan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bicta2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2021.bicta.org\/","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":"211","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":"32% - 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":"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)"}}]}}