{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:30:53Z","timestamp":1743103853304,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030267629"},{"type":"electronic","value":"9783030267636"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-26763-6_50","type":"book-chapter","created":{"date-parts":[[2019,7,29]],"date-time":"2019-07-29T23:18:04Z","timestamp":1564442284000},"page":"520-528","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Cloud Segmentation Based on Fused Fully Convolutional Networks"],"prefix":"10.1007","author":[{"given":"Jie","family":"An","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingfeng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwen","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,7,24]]},"reference":[{"key":"50_CR1","doi-asserted-by":"crossref","unstructured":"Chepfer, H., et al.: The GCM-oriented calipso cloud product (CALIPSO-GOCCP). J. Geophy. Res.: Atmos. 115(D4) (2010)","DOI":"10.1029\/2009JD012251"},{"key":"50_CR2","doi-asserted-by":"crossref","unstructured":"Dai, J., He, K., Sun, J.: BoxSup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1635\u20131643 (2015)","DOI":"10.1109\/ICCV.2015.191"},{"issue":"8","key":"50_CR3","doi-asserted-by":"publisher","first-page":"4591","DOI":"10.1109\/TGRS.2013.2265413","volume":"51","author":"CO Dumitru","year":"2013","unstructured":"Dumitru, C.O., Datcu, M.: Information content of very high resolution sar images: study of feature extraction and imaging parameters. IEEE Trans. Geosci. Remote Sens. 51(8), 4591\u20134610 (2013)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"50_CR4","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1016\/S0031-3203(01)00070-X","volume":"35","author":"A El Zaart","year":"2002","unstructured":"El Zaart, A., Ziou, D., Wang, S., Jiang, Q.: Segmentation of sar images. Pattern Recogn. 35(3), 713\u2013724 (2002)","journal-title":"Pattern Recogn."},{"issue":"11","key":"50_CR5","doi-asserted-by":"publisher","first-page":"2351","DOI":"10.1109\/LGRS.2015.2478256","volume":"12","author":"J Geng","year":"2015","unstructured":"Geng, J., Fan, J., Wang, H., Ma, X., Li, B., Chen, F.: High-resolution SAR image classification via deep convolutional autoencoders. IEEE Geosci. Remote Sens. Lett. 12(11), 2351\u20132355 (2015)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"50_CR6","doi-asserted-by":"crossref","unstructured":"Henry, C., Azimi, S.M., Merkle, N.: Road segmentation in SAR satellite images with deep fully-convolutional neural networks. arXiv preprint arXiv:1802.01445 (2018)","DOI":"10.1109\/LGRS.2018.2864342"},{"issue":"10","key":"50_CR7","doi-asserted-by":"publisher","first-page":"5585","DOI":"10.1109\/TGRS.2017.2710079","volume":"55","author":"L Jiao","year":"2017","unstructured":"Jiao, L., Liang, M., Chen, H., Yang, S., Liu, H., Cao, X.: Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 55(10), 5585\u20135599 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"50_CR8","doi-asserted-by":"crossref","unstructured":"Le Goff, M., Tourneret, J.Y., Wendt, H., Ortner, M., Spigai, M.: Deep learning for cloud detection (2017)","DOI":"10.1049\/cp.2017.0139"},{"key":"50_CR9","unstructured":"Li, R., et al.: DeepUNet: a deep fully convolutional network for pixel-level sea-land segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(11), 3954\u20133962 (2018). Fused-FCN for Automatic Cloud Segmentation 9"},{"key":"50_CR10","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: International Conference on Learning Representations (2015)"},{"key":"50_CR11","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks with identity mappings for high-resolution semantic segmentation. arXiv preprint arXiv:1611.06612 (2016)","DOI":"10.1109\/CVPR.2017.549"},{"key":"50_CR12","doi-asserted-by":"publisher","unstructured":"Liu, H., Zeng, D., Tian, Q.: Super-pixel cloud detection using hierarchical fusion CNN. In: 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), September 2018. https:\/\/doi.org\/10.1109\/bigmm.2018.8499091","DOI":"10.1109\/bigmm.2018.8499091"},{"issue":"6","key":"50_CR13","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1109\/LGRS.2013.2287295","volume":"11","author":"M Liu","year":"2014","unstructured":"Liu, M., Wu, Y., Zhao, W., Zhang, Q., Li, M., Liao, G.: Dempster\u2013Shafer fusion of multiple sparse representation and statistical property for SAR target configuration recognition. IEEE Geosci. Remote Sens. Lett. 11(6), 1106\u20131110 (2014)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"50_CR14","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":"50_CR15","doi-asserted-by":"crossref","unstructured":"Mohajerani, S., Krammer, T.A., Saeedi, P.: Cloud detection algorithm for remote sensing images using fully convolutional neural networks. arXiv preprint arXiv:1810.05782 (2018)","DOI":"10.1109\/MMSP.2018.8547095"},{"issue":"2","key":"50_CR16","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1109\/TGRS.2002.808301","volume":"41","author":"S Platnick","year":"2003","unstructured":"Platnick, S., et al.: The modis cloud products: algorithms and examples from terra. IEEE Trans. Geosci. Remote Sens. 41(2), 459\u2013473 (2003)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"50_CR17","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":"50_CR18","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.jag.2014.09.019","volume":"35","author":"A Samat","year":"2015","unstructured":"Samat, A., Gamba, P., Du, P., Luo, J.: Active extreme learning machines for quad- polarimetric SAR imagery classification. Int. J. Appl. Earth Obs. Geoinf. 35, 305\u2013319 (2015)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"50_CR19","doi-asserted-by":"crossref","unstructured":"Shi, M., Xie, F., Zi, Y., Yin, J.: Cloud detection of remote sensing images by deep learning. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 701\u2013704. IEEE (2016)","DOI":"10.1109\/IGARSS.2016.7729176"},{"issue":"2","key":"50_CR20","doi-asserted-by":"publisher","first-page":"342","DOI":"10.3390\/rs10020342","volume":"10","author":"Y Wang","year":"2018","unstructured":"Wang, Y., He, C., Liu, X., Liao, M.: A hierarchical fully convolutional network integrated with sparse and low-rank subspace representations for PolSAR imagery classification. Remote Sens. 10(2), 342 (2018)","journal-title":"Remote Sens."},{"issue":"8","key":"50_CR21","doi-asserted-by":"publisher","first-page":"3631","DOI":"10.1109\/JSTARS.2017.2686488","volume":"10","author":"F Xie","year":"2017","unstructured":"Xie, F., Shi, M., Shi, Z., Yin, J., Zhao, D.: Multilevel cloud detection in remote sensing images based on deep learning. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(8), 3631\u20133640 (2017)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"50_CR22","unstructured":"Yao, W., Marmanis, D., Datcu, M.: Semantic segmentation using deep neural networks for SAR and optical image pairs. In: Proceedings of the Big Data from Space, pp. 1\u20134 (2017)"},{"key":"50_CR23","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)"},{"issue":"6","key":"50_CR24","doi-asserted-by":"publisher","first-page":"877","DOI":"10.3390\/rs10060877","volume":"10","author":"Y Zi","year":"2018","unstructured":"Zi, Y., Xie, F., Jiang, Z.: A cloud detection method for landsat 8 images based on PCANet. Remote Sens. 10(6), 877 (2018)","journal-title":"Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-26763-6_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T13:16:57Z","timestamp":1617283017000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-26763-6_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030267629","9783030267636"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-26763-6_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanchang","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ic-ic.tongji.edu.cn\/2019\/index.htm","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":"ICIC Website","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"609","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":"217","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":"36% - 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":"3.43","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}