{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:23:58Z","timestamp":1743038638643,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811659393"},{"type":"electronic","value":"9789811659409"}],"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\/978-981-16-5940-9_23","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T23:04:08Z","timestamp":1631228648000},"page":"303-313","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semantic Segmentation of High Resolution Remote Sensing Images Based on Improved ResU-Net"],"prefix":"10.1007","author":[{"given":"Songyu","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Audebert, N., Le Saux, B., Lefevre, S.: Beyond RGB: very high resolution urban remote sensing with multimodal DeepNetworks. ISPRS J. Photogrammetry Remote Sens. 140, 20\u201332 (2017)","DOI":"10.1016\/j.isprsjprs.2017.11.011"},{"issue":"15","key":"23_CR2","doi-asserted-by":"publisher","first-page":"2350","DOI":"10.3390\/rs12152350","volume":"12","author":"J Ma","year":"2020","unstructured":"Ma, J., et al.: Building extraction of aerial images by a global and multi-scale encoder-decoder network. Remote Sens. 12(15), 2350 (2020)","journal-title":"Remote Sens."},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Ma, L., Liu, Y., Zhang, X., et al.: Deep learning in remote sensing applications: A meta-analysis and review. ISPRS J. Photogrammetry Remote Sens.152, 166\u2013177(2019)","DOI":"10.1016\/j.isprsjprs.2019.04.015"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Jiang, N., Li, J.: An improved semantic segmentation method for remote sensing images based on neural network. Traitement du Signal 37(2),\u00a0271\u2013278\u00a0(2020)","DOI":"10.18280\/ts.370213"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhang,Q., et al.: Gated convolutional neural network for semantic segmentation in high-resolution images. Remote Sens. 9(5), 446 (2017)","DOI":"10.3390\/rs9050446"},{"key":"23_CR6","unstructured":"Ding, L., Lorenzo, B.: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images. CoRR\u00a0abs\/2005.07232\u00a0(2020)"},{"issue":"5","key":"23_CR7","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/LGRS.2020.2988294","volume":"18","author":"H Li","year":"2021","unstructured":"Li, H., Qiu, K., Chen, L., et al.: SCAttNet: semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images. IEEE Geosci. Remote Sens. Lett. 18(5), 905\u2013909 (2021)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"4","key":"23_CR8","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"J Long","year":"2017","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"23_CR9","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR10","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 \u2014 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":"23_CR11","doi-asserted-by":"crossref","unstructured":"Alom, M, Z., Hasan, M., et al.: Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for MedicalImageSegmentation. CoRR\u00a0abs\/1802.06955\u00a0(2018).","DOI":"10.1109\/NAECON.2018.8556686"},{"issue":"10","key":"23_CR12","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu, Z., Cheng, J., Fu, H., et al.: CE-net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281\u20132292 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"23_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with Atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Cheng, G., Wang, Y., Xu, S., et al.: Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. IEEE Trans. Geosci. Remote Sens. 55(6),\u00a03322\u20133337\u00a0(2017)","DOI":"10.1109\/TGRS.2017.2669341"},{"issue":"5","key":"23_CR15","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1109\/JSTARS.2018.2810320","volume":"11","author":"G Chen","year":"2018","unstructured":"Chen, G., Zhang, X., Wang, Q., et al.: Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-high-resolution remote sensing images. IEEE J. Sel. Topics Appl. Earth Obser. Remote Sens. 11(5), 1633\u20131644 (2018)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obser. Remote Sens."},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. CoRR\u00a0abs\/2004.08790\u00a0(2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"issue":"9","key":"23_CR17","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.3390\/rs12091501","volume":"12","author":"C He","year":"2020","unstructured":"He, C., Li, S., Xiong, D., et al.: Remote sensing image semantic segmentation based on edge information guidance. Remote Sens. 12(9), 1501 (2020)","journal-title":"Remote Sens."},{"issue":"5","key":"23_CR18","doi-asserted-by":"publisher","first-page":"872","DOI":"10.3390\/rs12050872","volume":"12","author":"R Shang","year":"2020","unstructured":"Shang, R., Zhang, J., Jiao, L., et al.: Multi-scale adaptive feature fusion network for semantic segmentation in remote sensing images. Remote Sens. 12(5), 872 (2020)","journal-title":"Remote Sens."},{"issue":"5","key":"23_CR19","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-net. IEEE Geosci. Remote Sens. Lett. 15(5), 749\u2013753 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"1","key":"23_CR20","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3390\/rs11010020","volume":"11","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Liang, B., Ding, M., et al.: Dense semantic labeling with atrous spatial pyramid pooling and decoder for high-resolution remote sensing imagery. Remote Sens. 11(1), 20 (2019)","journal-title":"Remote Sens."},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Chen,L ,C., Papandreou, G., Kokkinos, I., et al.:DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4),\u00a0834\u2013848\u00a0(2018)","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Yang, M., et al.: DenseASPP for Semantic Segmentation in Street Scenes. CVPR, pp. 3684\u20133692 (2018)","DOI":"10.1109\/CVPR.2018.00388"},{"key":"23_CR23","unstructured":"Gerke, M. Use of the Stair Vision Library within the ISPRS 2D Semantic Labeling Benchmark (Vaihingen); Technical Report; University of Twente: Enschede, the Netherlands (2015)"}],"container-title":["Communications in Computer and Information Science","Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-5940-9_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:07:52Z","timestamp":1710256072000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-5940-9_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811659393","9789811659409"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-5940-9_23","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPCSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Pioneering Computer Scientists, Engineers and Educators","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 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpcsee2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2021.icpcsee.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"256","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":"81","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":"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)"}}]}}