{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T20:50:07Z","timestamp":1770497407232,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819984619","type":"print"},{"value":"9789819984626","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8462-6_22","type":"book-chapter","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:02:17Z","timestamp":1703530937000},"page":"267-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["UAM-Net: An Attention-Based Multi-level Feature Fusion UNet for\u00a0Remote Sensing Image Segmentation"],"prefix":"10.1007","author":[{"given":"Yiwen","family":"Cao","sequence":"first","affiliation":[]},{"given":"Nanfeng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Da-Han","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Shunzhi","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"22_CR1","first-page":"1","volume":"19","author":"H Bai","year":"2021","unstructured":"Bai, H., Cheng, J., Huang, X., Liu, S., Deng, C.: HCANet: a hierarchical context aggregation network for semantic segmentation of high-resolution remote sensing images. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2021)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"22_CR2","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5659\u20135667 (2017)","DOI":"10.1109\/CVPR.2017.667"},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C.: ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogramm. Remote. Sens. 162, 94\u2013114 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Griffiths, P., Nendel, C., Hostert, P.: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sens. Environ. 220, 135\u2013151 (2019)","DOI":"10.1016\/j.rse.2018.10.031"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"22_CR8","first-page":"1","volume":"19","author":"R Li","year":"2021","unstructured":"Li, R., Zheng, S., Duan, C., Su, J., Zhang, C.: Multistage attention resU-Net for semantic segmentation of fine-resolution remote sensing images. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2021)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"22_CR9","first-page":"1","volume":"60","author":"R Li","year":"2021","unstructured":"Li, R., et al.: Multi attention network for semantic segmentation of fine-resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201313 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.isprsjprs.2021.09.005","volume":"181","author":"R Li","year":"2021","unstructured":"Li, R., Zheng, S., Zhang, C., Duan, C., Wang, L., Atkinson, P.M.: ABCNet: attentive bilateral contextual network for efficient semantic segmentation of fine-resolution remotely sensed imagery. ISPRS J. Photogramm. Remote. Sens. 181, 84\u201398 (2021)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Misra, D., Nalamada, T., Arasanipalai, A.U., Hou, Q.: Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3139\u20133148 (2021)","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"22_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1007\/978-3-030-00934-2_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"H Oda","year":"2018","unstructured":"Oda, H., et al.: BESNet: boundary-enhanced segmentation of cells in histopathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 228\u2013236. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_26"},{"key":"22_CR14","unstructured":"Park, J., Woo, S., Lee, J., Kweon, I.S.: BAM: bottleneck attention module. In: British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK, September 3\u20136, 2018. p. 147. BMVA Press (2018)"},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.isprsjprs.2018.08.007","volume":"145","author":"MCA Picoli","year":"2018","unstructured":"Picoli, M.C.A., et al.: Big earth observation time series analysis for monitoring Brazilian agriculture. ISPRS J. Photogramm. Remote. Sens. 145, 328\u2013339 (2018)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"22_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":"Olaf Ronneberger","year":"2015","unstructured":"Ronneberger, Olaf, Fischer, Philipp, Brox, Thomas: U-Net: convolutional networks for biomedical image segmentation. In: Navab, Nassir, Hornegger, Joachim, Wells, William M.., Frangi, Alejandro 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":"22_CR17","doi-asserted-by":"publisher","first-page":"25415","DOI":"10.1007\/s11356-020-08984-x","volume":"27","author":"A Samie","year":"2020","unstructured":"Samie, A., et al.: Examining the impacts of future land use\/land cover changes on climate in Punjab province, Pakistan: implications for environmental sustainability and economic growth. Environ. Sci. Pollut. Res. 27, 25415\u201325433 (2020)","journal-title":"Environ. Sci. Pollut. Res."},{"key":"22_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111322","volume":"237","author":"XY Tong","year":"2020","unstructured":"Tong, X.Y., et al.: Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sens. Environ. 237, 111322 (2020)","journal-title":"Remote Sens. Environ."},{"issue":"5","key":"22_CR19","doi-asserted-by":"publisher","first-page":"446","DOI":"10.3390\/rs9050446","volume":"9","author":"H Wang","year":"2017","unstructured":"Wang, H., Wang, Y., Zhang, Q., Xiang, S., Pan, C.: Gated convolutional neural network for semantic segmentation in high-resolution images. Remote Sens. 9(5), 446 (2017)","journal-title":"Remote Sens."},{"key":"22_CR20","first-page":"1","volume":"19","author":"L Wang","year":"2022","unstructured":"Wang, L., Li, R., Duan, C., Zhang, C., Meng, X., Fang, S.: A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"16","key":"22_CR21","doi-asserted-by":"publisher","first-page":"3065","DOI":"10.3390\/rs13163065","volume":"13","author":"L Wang","year":"2021","unstructured":"Wang, L., Li, R., Wang, D., Duan, C., Wang, T., Meng, X.: Transformer meets convolution: a bilateral awareness network for semantic segmentation of very fine resolution urban scene images. Remote Sens. 13(16), 3065 (2021)","journal-title":"Remote Sens."},{"key":"22_CR22","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.isprsjprs.2022.06.008","volume":"190","author":"L Wang","year":"2022","unstructured":"Wang, L., et al.: UNetFormer: a UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery. ISPRS J. Photogramm. Remote. Sens. 190, 196\u2013214 (2022)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"22_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Sanghyun Woo","year":"2018","unstructured":"Woo, Sanghyun, Park, Jongchan, Lee, Joon-Young., Kweon, In So.: CBAM: convolutional block attention module. In: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, Weiss, Yair (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"22_CR24","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/j.rse.2017.08.030","volume":"204","author":"H Yin","year":"2018","unstructured":"Yin, H., Pflugmacher, D., Li, A., Li, Z., Hostert, P.: Land use and land cover change in inner Mongolia-understanding the effects of China\u2019s re-vegetation programs. Remote Sens. Environ. 204, 918\u2013930 (2018)","journal-title":"Remote Sens. Environ."},{"key":"22_CR25","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","volume":"221","author":"C Zhang","year":"2019","unstructured":"Zhang, C., et al.: Joint deep learning for land cover and land use classification. Remote Sens. Environ. 221, 173\u2013187 (2019)","journal-title":"Remote Sens. Environ."},{"issue":"5","key":"22_CR26","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."},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8462-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:05:05Z","timestamp":1703531105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8462-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"ISBN":["9789819984619","9789819984626"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8462-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"26 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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)"}}]}}