{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T14:44:10Z","timestamp":1792421050640,"version":"4.5.0"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031149023","type":"print"},{"value":"9783031149030","type":"electronic"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"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_35","type":"book-chapter","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T19:03:00Z","timestamp":1666119780000},"page":"326-333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Combining Spatial-Spectral Features for\u00a0Hyperspectral Image Few-Shot Classification"],"prefix":"10.1007","author":[{"given":"Yonghao","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiong","family":"Ran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"35_CR1","doi-asserted-by":"publisher","unstructured":"Chen, Y., Liu, Z., Xu, H., Darrell, T., Wang, X.: Meta-baseline: exploring simple meta-learning for few-shot learning. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9042\u20139051 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00893","DOI":"10.1109\/ICCV48922.2021.00893"},{"issue":"10","key":"35_CR2","doi-asserted-by":"publisher","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","volume":"54","author":"Y Chen","year":"2016","unstructured":"Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232\u20136251 (2016). https:\/\/doi.org\/10.1109\/TGRS.2016.2584107","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"35_CR3","doi-asserted-by":"publisher","unstructured":"Gao, K., Liu, B., Yu, X., Qin, J., Zhang, P., Tan, X.: Deep relation network for hyperspectral image few-shot classification. Remote Sens. 12(6) (2020). https:\/\/doi.org\/10.3390\/rs12060923","DOI":"10.3390\/rs12060923"},{"issue":"1","key":"35_CR4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1109\/TGRS.2019.2934760","volume":"58","author":"J He","year":"2020","unstructured":"He, J., Zhao, L., Yang, H., Zhang, M., Li, W.: Hsi-bert: hyperspectral image classification using the bidirectional encoder representation from transformers. IEEE Trans. Geosci. Remote Sens. 58(1), 165\u2013178 (2020). https:\/\/doi.org\/10.1109\/TGRS.2019.2934760","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"7","key":"35_CR5","doi-asserted-by":"publisher","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","volume":"59","author":"D Hong","year":"2021","unstructured":"Hong, D., Gao, L., Yao, J., Zhang, B., Plaza, A., Chanussot, J.: Graph convolutional networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(7), 5966\u20135978 (2021). https:\/\/doi.org\/10.1109\/TGRS.2020.3015157","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"35_CR6","doi-asserted-by":"publisher","unstructured":"Hong, D., et al.: More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Trans. Geosci. Remote Sens. 59(5), 4340\u20134354 (2021). https:\/\/doi.org\/10.1109\/TGRS.2020.3016820","DOI":"10.1109\/TGRS.2020.3016820"},{"key":"35_CR7","doi-asserted-by":"publisher","unstructured":"Hong, D., et al.: Spectralformer: rethinking hyperspectral image classification with transformers. IEEE Trans. Geosci. Remote Sens. 60, 1\u201315 (2022). https:\/\/doi.org\/10.1109\/TGRS.2021.3130716","DOI":"10.1109\/TGRS.2021.3130716"},{"issue":"9","key":"35_CR8","doi-asserted-by":"publisher","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","volume":"57","author":"S Li","year":"2019","unstructured":"Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.: Deep learning for hyperspectral image classification: an overview. IEEE Trans. Geosci. Remote Sens. 57(9), 6690\u20136709 (2019). https:\/\/doi.org\/10.1109\/TGRS.2019.2907932","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"35_CR9","doi-asserted-by":"publisher","unstructured":"Li, Y., Zhang, H., Shen, Q.: Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9(1) (2017). https:\/\/doi.org\/10.3390\/rs9010067","DOI":"10.3390\/rs9010067"},{"key":"35_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3057066","volume":"60","author":"Z Li","year":"2022","unstructured":"Li, Z., Liu, M., Chen, Y., Xu, Y., Li, W., Du, Q.: Deep cross-domain few-shot learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1\u201318 (2022). https:\/\/doi.org\/10.1109\/TGRS.2021.3057066","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"35_CR11","doi-asserted-by":"publisher","first-page":"2290","DOI":"10.1109\/TGRS.2018.2872830","volume":"57","author":"B Liu","year":"2019","unstructured":"Liu, B., Yu, X., Yu, A., Zhang, P., Wan, G., Wang, R.: Deep few-shot learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(4), 2290\u20132304 (2019). https:\/\/doi.org\/10.1109\/TGRS.2018.2872830","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"35_CR12","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Domain adaptation with randomized multilinear adversarial networks. arXiv preprint arXiv:1705.10667 (2017)"},{"key":"35_CR13","doi-asserted-by":"publisher","unstructured":"Melgani, F., Bruzzone, L.: Support vector machines for classification of hyperspectral remote-sensing images. In: IEEE International Geoscience and Remote Sensing Symposium, vol. 1, pp. 506\u2013508 (2002). https:\/\/doi.org\/10.1109\/IGARSS.2002.1025088","DOI":"10.1109\/IGARSS.2002.1025088"},{"issue":"5","key":"35_CR14","doi-asserted-by":"publisher","first-page":"3232","DOI":"10.1109\/TGRS.2019.2951160","volume":"58","author":"H Sun","year":"2020","unstructured":"Sun, H., Zheng, X., Lu, X., Wu, S.: Spectral-spatial attention network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 58(5), 3232\u20133245 (2020). https:\/\/doi.org\/10.1109\/TGRS.2019.2951160","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"35_CR15","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MGRS.2016.2548504","volume":"4","author":"D Tuia","year":"2016","unstructured":"Tuia, D., Persello, C., Bruzzone, L.: Domain adaptation for the classification of remote sensing data: an overview of recent advances. IEEE Geosci. Remote Sens. Magaz. 4(2), 41\u201357 (2016). https:\/\/doi.org\/10.1109\/MGRS.2016.2548504","journal-title":"IEEE Geosci. Remote Sens. Magaz."}],"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_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T14:27:37Z","timestamp":1792420057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-14903-0_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031149023","9783031149030"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14903-0_35","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}