{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:21:45Z","timestamp":1743085305547,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030688202"},{"type":"electronic","value":"9783030688219"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68821-9_34","type":"book-chapter","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T20:03:56Z","timestamp":1613851436000},"page":"380-396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["R2SN: Refined Semantic Segmentation Network of City Remote Sensing Image"],"prefix":"10.1007","author":[{"given":"Chenglong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"issue":"8\u201310","key":"34_CR1","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1080\/01431161.2017.1294781","volume":"38","author":"OS Ahmed","year":"2017","unstructured":"Ahmed, O.S., et al.: Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle. Int. J. Remote Sens. 38(8\u201310), 2037\u20132052 (2017)","journal-title":"Int. J. Remote Sens."},{"issue":"12","key":"34_CR2","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."},{"issue":"1","key":"34_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"34_CR4","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"34_CR5","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":"34_CR6","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. Photogrammetry Remote Sens. 162, 94\u2013114 (2020)","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"34_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":"34_CR8","unstructured":"Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)"},{"key":"34_CR9","first-page":"1","volume":"14","author":"H Li","year":"2020","unstructured":"Li, H., Qiu, K., Chen, L., Mei, X., Hong, L., Tao, C.: Scattnet: semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images. IEEE Geosci. Remote Sens. Lett. 14, 1\u20135 (2020)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Li, H., Xiong, P., Fan, H., Sun, J.: DfaNet: deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9522\u20139531 (2019)","DOI":"10.1109\/CVPR.2019.00975"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1925\u20131934 (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"34_CR13","doi-asserted-by":"publisher","unstructured":"Liu, W., Liu, X., Ma, H., Cheng, P.: Beyond human-level license plate super-resolution with progressive vehicle search and domain priori GAN. In: Proceedings of the 25th ACM International Conference on Multimedia. MM 2017, Association for Computing Machinery, New York, NY, USA, pp. 1618\u20131626 (2017). https:\/\/doi.org\/10.1145\/3123266.3123422","DOI":"10.1145\/3123266.3123422"},{"issue":"3","key":"34_CR14","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TMM.2017.2751966","volume":"20","author":"X Liu","year":"2018","unstructured":"Liu, X., Liu, W., Mei, T., Ma, H.: PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans. Multimedia 20(3), 645\u2013658 (2018). https:\/\/doi.org\/10.1109\/TMM.2017.2751966","journal-title":"IEEE Trans. Multimedia"},{"key":"34_CR15","doi-asserted-by":"publisher","unstructured":"Liu, X., Zhang, M., Liu, W., Song, J., Mei, T.: BraidNet: braiding semantics and details for accurate human parsing, October 2019. https:\/\/doi.org\/10.1145\/3343031.3350857","DOI":"10.1145\/3343031.3350857"},{"issue":"10","key":"34_CR16","doi-asserted-by":"publisher","first-page":"3232","DOI":"10.3390\/s18103232","volume":"18","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Ren, Q., Geng, J., Ding, M., Li, J.: Efficient patch-wise semantic segmentation for large-scale remote sensing images. Sensors 18(10), 3232 (2018)","journal-title":"Sensors"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"34_CR18","doi-asserted-by":"publisher","first-page":"473","DOI":"10.5194\/isprs-annals-III-3-473-2016","volume":"3","author":"D Marmanis","year":"2016","unstructured":"Marmanis, D., Wegner, J.D., Galliani, S., Schindler, K., Datcu, M., Stilla, U.: Semantic segmentation of aerial images with an ensemble of CNSs. ISPRS Ann. Photogram. Remote Sens. Spatial Inf. Sci. 3, 473\u2013480 (2016)","journal-title":"ISPRS Ann. Photogram. Remote Sens. Spatial Inf. Sci."},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Nie, W.Z., Liu, A.A., Zhao, S., Gao, Y.: Deep correlated joint network for 2-d image-based 3-d model retrieval. IEEE Trans. Cybernet. (2020)","DOI":"10.1109\/TCYB.2020.2995415"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Nie, W., Jia, W., Li, W., Liu, A., Zhao, S.: 3d pose estimation based on reinforce learning for 2d image-based 3d model retrieval. IEEE Trans. Multimedia (2020)","DOI":"10.1109\/TMM.2020.2991532"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large kernel matters-improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4353\u20134361 (2017)","DOI":"10.1109\/CVPR.2017.189"},{"key":"34_CR22","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"34_CR23","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":"34_CR24","unstructured":"Sun, Y., et al.: Synthetic training for monocular human mesh recovery, October 2020"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Vakalopoulou, M., Karantzalos, K., Komodakis, N., Paragios, N.: Building detection in very high resolution multispectral data with deep learning features. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1873\u20131876. IEEE (2015)","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"34_CR26","doi-asserted-by":"publisher","first-page":"7549","DOI":"10.1109\/TIP.2020.3004249","volume":"29","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Liu, X., Liu, W., Liu, A., Liu, W., Mei, T.: Metasearch: incremental product search via deep meta-learning. IEEE Trans. Image Process. 29, 7549\u20137564 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.3004249","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"34_CR27","first-page":"271","volume":"3","author":"W Wang","year":"2016","unstructured":"Wang, W., Yang, N., Zhang, Y., Wang, F., Cao, T., Eklund, P.: A review of road extraction from remote sensing images. J. Traff. Transp. Eng. (Eng. Ed.) 3(3), 271\u2013282 (2016)","journal-title":"J. Traff. Transp. Eng. (Eng. Ed.)"},{"key":"34_CR28","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Learning a discriminative feature network for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1857\u20131866 (2018)","DOI":"10.1109\/CVPR.2018.00199"},{"key":"34_CR29","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)"},{"key":"34_CR30","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2528\u20132535. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539957"},{"key":"34_CR31","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. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68821-9_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T20:14:34Z","timestamp":1613852074000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68821-9_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030688202","9783030688219"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68821-9_34","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":"21 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","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":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}