{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:14:56Z","timestamp":1760890496136,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031149023"},{"type":"electronic","value":"9783031149030"}],"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\/978-3-031-14903-0_24","type":"book-chapter","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T23:03:00Z","timestamp":1666134180000},"page":"220-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["RSMatch: Semi-supervised Learning with\u00a0Adaptive Category-Related Pseudo Labeling for\u00a0Remote Sensing Scene Classification"],"prefix":"10.1007","author":[{"given":"Weiquan","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangrong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.1109\/JSTARS.2021.3051569","volume":"14","author":"X Tang","year":"2021","unstructured":"Tang, X., Ma, Q., Zhang, X., Liu, F., Ma, J., Jiao, L.: Attention consistent network for remote sensing scene classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 2030\u20132045 (2021). https:\/\/doi.org\/10.1109\/JSTARS.2021.3051569","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"24_CR2","doi-asserted-by":"publisher","unstructured":"Yang, Y., et al.: AR2Det: an accurate and real-time rotational one-stage ship detector in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022). Art no. 5605414. https:\/\/doi.org\/10.1109\/TGRS.2021.3092433","DOI":"10.1109\/TGRS.2021.3092433"},{"key":"24_CR3","doi-asserted-by":"publisher","unstructured":"Tang, X., et al.: An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning. IEEE Trans. Geosci. Remote Sens. 60, 1\u201315 (2022). Art no. 5609715. https:\/\/doi.org\/10.1109\/TGRS.2021.3106381","DOI":"10.1109\/TGRS.2021.3106381"},{"issue":"5","key":"24_CR4","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","volume":"56","author":"G Cheng","year":"2018","unstructured":"Cheng, G., Yang, C., Yao, X., Guo, L., Han, J.: When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Trans. Geosci. Remote Sens. 56(5), 2811\u20132821 (2018). https:\/\/doi.org\/10.1109\/TGRS.2017.2783902","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"24_CR5","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","volume":"57","author":"Q Wang","year":"2019","unstructured":"Wang, Q., Liu, S., Chanussot, J., Li, X.: Scene classification with recurrent attention of VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 57(2), 1155\u20131167 (2019). https:\/\/doi.org\/10.1109\/TGRS.2018.2864987","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"24_CR6","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/LGRS.2019.2894399","volume":"16","author":"X Liu","year":"2019","unstructured":"Liu, X., Zhou, Y., Zhao, J., Yao, R., Liu, B., Zheng, Y.: Siamese convolutional neural networks for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 16(8), 1200\u20131204 (2019). https:\/\/doi.org\/10.1109\/LGRS.2019.2894399","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"12","key":"24_CR7","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.1109\/LGRS.2020.3014108","volume":"18","author":"D Guo","year":"2021","unstructured":"Guo, D., Xia, Y., Luo, X.: GAN-based semisupervised scene classification of remote sensing image. IEEE Geosci. Remote Sens. Lett. 18(12), 2067\u20132071 (2021). https:\/\/doi.org\/10.1109\/LGRS.2020.3014108","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"24_CR8","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1109\/JSTARS.2020.2978864","volume":"13","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Zhang, M., Pan, B., Shi, Z.: Semisupervised center loss for remote sensing image scene classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 1362\u20131373 (2020). https:\/\/doi.org\/10.1109\/JSTARS.2020.2978864","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"24_CR9","doi-asserted-by":"publisher","first-page":"11643","DOI":"10.1109\/JSTARS.2021.3126082","volume":"14","author":"P Gomez","year":"2021","unstructured":"Gomez, P., Meoni, G.: MSMatch: semisupervised multispectral scene classification with few labels. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 11643\u201311654 (2021). https:\/\/doi.org\/10.1109\/JSTARS.2021.3126082","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"6","key":"24_CR10","volume":"8","author":"Y Li","year":"2018","unstructured":"Li, Y., Zhang, H., Xue, X., et al.: Deep learning for remote sensing image classification: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 8(6), e1264 (2018)","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Disc."},{"key":"24_CR11","unstructured":"Yang, X., Song, Z., King, I., et al.: A survey on deep semi-supervised learning. arXiv preprint arXiv:2103.00550 (2021)"},{"key":"24_CR12","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"24_CR13","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn, K., Berthelot, D., Carlini, N., et al.: FixMatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596\u2013608 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"24_CR14","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"24_CR15","unstructured":"Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in Neural Information Processing Systems, vol. 17 (2004)"},{"key":"24_CR16","unstructured":"Sutskever, I., Martens, J., Dahl, G., et al.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139\u20131147. PMLR (2013)"},{"key":"24_CR17","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., et al.: RandAugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702\u2013703 (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: 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"}],"container-title":["IFIP Advances in Information and Communication Technology","Intelligence Science IV"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14903-0_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T07:06:19Z","timestamp":1672383979000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14903-0_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031149023","9783031149030"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14903-0_24","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligence Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.intsci.ac.cn\/icis2022\/home\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","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":"85","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":"44","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":"5","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":"52% - 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)"}}]}}