{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:50:19Z","timestamp":1742914219927,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030317225"},{"type":"electronic","value":"9783030317232"}],"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\/978-3-030-31723-2_51","type":"book-chapter","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T00:05:31Z","timestamp":1572480331000},"page":"596-606","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-scale Densely 3D CNN for Hyperspectral Image Classification"],"prefix":"10.1007","author":[{"given":"Yong","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Qin","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Dongyue","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,31]]},"reference":[{"issue":"11","key":"51_CR1","doi-asserted-by":"publisher","first-page":"4032","DOI":"10.1109\/JSTARS.2018.2872969","volume":"11","author":"B Tu","year":"2018","unstructured":"Tu, B., et al.: KNN-based representation of superpixels for hyperspectral image classification. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 11(11), 4032\u20134047 (2018)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"doi-asserted-by":"publisher","unstructured":"Zhou, S., Xue, Z., Du, P.: Semisupervised stacked autoencoder with cotraining for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57, 1\u201314 (2019) https:\/\/doi.org\/10.1109\/tgrs.2018.2888485","key":"51_CR2","DOI":"10.1109\/tgrs.2018.2888485"},{"issue":"6","key":"51_CR3","doi-asserted-by":"publisher","first-page":"2632","DOI":"10.1109\/JSTARS.2015.2427656","volume":"8","author":"Y Yuan","year":"2015","unstructured":"Yuan, Y., Feng, Y., Lu, X.: Projection-based NMF for hyperspectral unmixing. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 8(6), 2632\u20132643 (2015)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"issue":"12","key":"51_CR4","doi-asserted-by":"publisher","first-page":"3904","DOI":"10.1016\/j.patcog.2015.05.024","volume":"48","author":"W Li","year":"2015","unstructured":"Li, W., Du, Q., Zhang, B.: Combined sparse and collaborative representation for hyperspectral target detection. Pattern Recogn. 48(12), 3904\u20133916 (2015)","journal-title":"Pattern Recogn."},{"issue":"12","key":"51_CR5","doi-asserted-by":"publisher","first-page":"4172","DOI":"10.1109\/TGRS.2007.905311","volume":"45","author":"L Zhang","year":"2007","unstructured":"Zhang, L., Zhong, Y., Huang, B., et al.: Dimensionality reduction based on clonal selection for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 45(12), 4172\u20134186 (2007)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"51_CR6","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1109\/TGRS.2017.2765364","volume":"56","author":"L He","year":"2018","unstructured":"He, L., Li, J., Liu, C., et al.: Recent advances on spectral\u2013spatial hyperspectral image classification: an overview and new guidelines. IEEE Trans. Geosci. Remote Sens. 56(3), 1579\u20131597 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"6","key":"51_CR7","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1109\/TGRS.2008.916090","volume":"46","author":"E Blanzieri","year":"2008","unstructured":"Blanzieri, E., Melgani, F.: Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Trans. Geosci. Remote Sens. 46(6), 1804\u20131811 (2008)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"51_CR8","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","volume":"42","author":"F Melgani","year":"2004","unstructured":"Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778\u20131790 (2004)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"10","key":"51_CR9","doi-asserted-by":"publisher","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","volume":"49","author":"Y Chen","year":"2011","unstructured":"Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49(10), 3973\u20133985 (2011)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"51_CR10","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.patcog.2018.03.027","volume":"81","author":"Y Shao","year":"2018","unstructured":"Shao, Y., Sang, N., Gao, C., et al.: Spatial and class structure regularized sparse representation graph for semi-supervised hyperspectral image classification. Pattern Recogn. 81, 81\u201394 (2018)","journal-title":"Pattern Recogn."},{"key":"51_CR11","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1155\/2015\/258619","volume":"2015","author":"W Hu","year":"2015","unstructured":"Hu, W., Huang, Y., Wei, L., et al.: Deep convolutional neural networks for hyperspectral image classification. J. Sens. 2015, 12 (2015)","journal-title":"J. Sens."},{"doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., et al.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4959\u20134962 (2015)","key":"51_CR12","DOI":"10.1109\/IGARSS.2015.7326945"},{"issue":"8","key":"51_CR13","doi-asserted-by":"publisher","first-page":"4520","DOI":"10.1109\/TGRS.2017.2693346","volume":"55","author":"S Mei","year":"2017","unstructured":"Mei, S., Ji, J., Hou, J., et al.: Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55(8), 4520\u20134533 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"51_CR14","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","volume":"55","author":"W Li","year":"2017","unstructured":"Li, W., Wu, G., Zhang, F., et al.: Hyperspectral image classification using deep pixel-pair features. IEEE Trans. Geosci. Remote Sens. 55(2), 844\u2013853 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"51_CR15","doi-asserted-by":"publisher","first-page":"4781","DOI":"10.1109\/TGRS.2018.2837142","volume":"56","author":"X Ma","year":"2018","unstructured":"Ma, X., Fu, A., Wang, J., Wang, H., Yin, B.: Hyperspectral image classification based on deep deconvolution network with skip architecture. IEEE Trans. Geosci. Remote Sens. 56(8), 4781\u20134791 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"9","key":"51_CR16","doi-asserted-by":"publisher","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","volume":"56","author":"L Zhu","year":"2018","unstructured":"Zhu, L., Chen, Y., Ghamisi, P., et al.: Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(9), 5046\u20135063 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"10","key":"51_CR17","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., et al.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232\u20136251 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"He, M., Li, B., Chen, H.: Multi-scale 3D deep convolutional neural network for hyperspectral image classification. In: 2017 IEEE International Conference on Image Processing (ICIP) (2017)","key":"51_CR18","DOI":"10.1109\/ICIP.2017.8297014"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., et al.: Densely connected convolutional networks In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","key":"51_CR19","DOI":"10.1109\/CVPR.2017.243"},{"doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., et al.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","key":"51_CR20","DOI":"10.1109\/ICCV.2015.510"},{"unstructured":"AVIRIS NW Indiana\u2019s Indian Pines 1992 Data Set. http:\/\/cobweb.ecn.purdue.edu\/biehl\/MultiSpec\/documentation.html. Accessed 21 Mar 2019","key":"51_CR21"},{"doi-asserted-by":"crossref","unstructured":"Gualtieri, J.A., Cromp, R.F.: Support vector machines for hyperspectral remote sensing classification. In: 27th AIPR Workshop: Advances in Computer-Assisted Recognition. International Society for Optics and Photonics, vol. 3584, pp. 221\u2013233 (1999)","key":"51_CR22","DOI":"10.1117\/12.339824"},{"key":"51_CR23","doi-asserted-by":"publisher","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","volume":"113","author":"A Plaza","year":"2009","unstructured":"Plaza, A., Benediktsson, J.A., Boardman, J.W., et al.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113, S110\u2013S122 (2009)","journal-title":"Remote Sens. Environ."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31723-2_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:27:34Z","timestamp":1730334454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31723-2_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030317225","9783030317232"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31723-2_51","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":"31 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv2019.com\/en\/index.html","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"412","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":"165","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":"40% - 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":"4","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":"4","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)"}}]}}