{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:29:47Z","timestamp":1742916587494,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030348687"},{"type":"electronic","value":"9783030348694"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-34869-4_60","type":"book-chapter","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T00:02:57Z","timestamp":1574640177000},"page":"551-558","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Approach of Transferring Pre-trained Deep Convolutional Neural Networks for Aerial Scene Classification"],"prefix":"10.1007","author":[{"given":"Nilakshi","family":"Devi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhogeswar","family":"Borah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,25]]},"reference":[{"key":"60_CR1","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.isprsjprs.2018.01.023","volume":"138","author":"RM Anwer","year":"2018","unstructured":"Anwer, R.M., Khan, F.S., van de Weijer, J., Molinier, M., Laaksonen, J.: Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. ISPRS J. Photogramm. Remote Sens. 138, 74\u201385 (2018)","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"60_CR2","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L.: Land use classification in remote sensing images by convolutional neural networks. arXiv preprint arXiv:1508.00092 (2015)"},{"issue":"11","key":"60_CR3","doi-asserted-by":"publisher","first-page":"14680","DOI":"10.3390\/rs71114680","volume":"7","author":"F Hu","year":"2015","unstructured":"Hu, F., Xia, G.S., Hu, J., Zhang, L.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11), 14680\u201314707 (2015)","journal-title":"Remote Sens."},{"issue":"10","key":"60_CR4","doi-asserted-by":"publisher","first-page":"5653","DOI":"10.1109\/TGRS.2017.2711275","volume":"55","author":"E Li","year":"2017","unstructured":"Li, E., Xia, J., Du, P., Lin, C., Samat, A.: Integrating multilayer features of convolutional neural networks for remote sensing scene classification. IEEE Trans. Geosci. Remote Sens. 55(10), 5653\u20135665 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"60_CR5","unstructured":"Liu, Q., Hang, R., Song, H., Zhu, F., Plaza, J., Plaza, A.: Adaptive deep pyramid matching for remote sensing scene classification. arXiv preprint arXiv:1611.03589 (2016)"},{"issue":"8","key":"60_CR6","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1080\/01431161.2011.608740","volume":"33","author":"G Sheng","year":"2012","unstructured":"Sheng, G., Yang, W., Xu, T., Sun, H.: High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int. J. Remote Sens. 33(8), 2395\u20132412 (2012)","journal-title":"Int. J. Remote Sens."},{"issue":"9","key":"60_CR7","doi-asserted-by":"publisher","first-page":"4104","DOI":"10.1109\/JSTARS.2017.2705419","volume":"10","author":"G Wang","year":"2017","unstructured":"Wang, G., Fan, B., Xiang, S., Pan, C.: Aggregating rich hierarchical features for scene classification in remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(9), 4104\u20134115 (2017)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"3","key":"60_CR8","doi-asserted-by":"publisher","first-page":"225","DOI":"10.3390\/rs9030225","volume":"9","author":"J Wang","year":"2017","unstructured":"Wang, J., Luo, C., Huang, H., Zhao, H., Wang, S.: Transferring pre-trained deep cnns for remote scene classification with general features learned from linear pca network. Remote Sens. 9(3), 225 (2017)","journal-title":"Remote Sens."},{"key":"60_CR9","doi-asserted-by":"crossref","unstructured":"Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 270\u2013279. ACM (2010)","DOI":"10.1145\/1869790.1869829"},{"key":"60_CR10","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320\u20133328 (2014)"},{"issue":"5","key":"60_CR11","doi-asserted-by":"publisher","first-page":"734","DOI":"10.3390\/rs10050734","volume":"10","author":"D Zeng","year":"2018","unstructured":"Zeng, D., Chen, S., Chen, B., Li, S.: Improving remote sensing scene classification by integrating global-context and local-object features. Remote Sens. 10(5), 734 (2018)","journal-title":"Remote Sens."},{"issue":"5","key":"60_CR12","doi-asserted-by":"publisher","first-page":"494","DOI":"10.3390\/rs11050494","volume":"11","author":"W Zhang","year":"2019","unstructured":"Zhang, W., Tang, P., Zhao, L.: Remote sensing image scene classification using CNN-CapsNet. Remote Sens. 11(5), 494 (2019)","journal-title":"Remote Sens."},{"issue":"11","key":"60_CR13","doi-asserted-by":"publisher","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","volume":"12","author":"Q Zou","year":"2015","unstructured":"Zou, Q., Ni, L., Zhang, T., Wang, Q.: Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12(11), 2321\u20132325 (2015)","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-34869-4_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:17:23Z","timestamp":1710170243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-34869-4_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030348687","9783030348694"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-34869-4_60","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":"25 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PReMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Machine Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tezpur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"17 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"premi2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tezu.ernet.in\/~premi2019\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"341","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":"131","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":"38% - 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":"2","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","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":"Yes","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"}]}}