{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T15:10:26Z","timestamp":1744902626757,"version":"3.37.3"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T00:00:00Z","timestamp":1526860800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Science and Technology Innovation Team of Henan Province Supports Project","award":["17IRTSTHN014"],"award-info":[{"award-number":["17IRTSTHN014"]}]},{"name":"Key Scientific Research Projects in Henan Province","award":["18A510018","18A510019"],"award-info":[{"award-number":["18A510018","18A510019"]}]},{"name":"Science and Technology Project of Henan Province","award":["182102210110","182102210111"],"award-info":[{"award-number":["182102210110","182102210111"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Wireless Com Network"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s13638-018-1133-2","type":"journal-article","created":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T08:23:38Z","timestamp":1526891018000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Classification methods of a small sample target object in the sky based on the higher layer visualizing feature and transfer learning deep networks"],"prefix":"10.1186","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1222-3204","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"first","affiliation":[]},{"given":"Hongbing","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Xinling","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Pengge","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Yuxin","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Zhengxiang","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Zhaoyu","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,5,21]]},"reference":[{"issue":"2","key":"1133_CR1","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"A Krizhevsky, I Sutskever, GE Hinton, ImageNet classification with deep convolutional neural networks. Int. Conf. Neural Inf. Process. Syst. Curran Assoc. Inc 60(2), 1097\u20131105 (2012). \n                    https:\/\/doi.org\/10.1145\/3065386","journal-title":"Int. Conf. Neural Inf. Process. Syst. Curran Assoc. Inc"},{"issue":"8","key":"1133_CR2","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Y Bengio, A Clurville, P Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013). \n                    https:\/\/doi.org\/10.1109\/TPAMI.2013.50","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7553","key":"1133_CR3","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Y Lecun, Y Bengio, G Hinton, Deep learning. Nature 521(7553), 436\u2013444 (2015). \n                    https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"1133_CR4","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"J Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015). \n                    https:\/\/doi.org\/10.1016\/j.neunet.2014.09.003","journal-title":"Neural Netw."},{"key":"1133_CR5","doi-asserted-by":"publisher","unstructured":"J Masci, U Meier, C Dan, J Schmidhuber, in Proceedings of the 21\n                           \n                    st\n                  \n                           International Conference on Artificial Neural Networks, Espoo, 6791. Stacked convolutional auto-encoders for hierarchical feature extraction (2011), pp. 52\u201359. \n                    https:\/\/doi.org\/10.1007\/978-3-642-21735-7_7","DOI":"10.1007\/978-3-642-21735-7_7"},{"issue":"4","key":"1133_CR6","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1109\/TGRS.2014.2357078","volume":"53","author":"F Zhang","year":"2015","unstructured":"F Zhang, B Du, L Zhang, Saliency-guided unsupervised feature learning for scene classification. IEEE Trans. Geosci. Remote Sens. 53(4), 2175\u20132184 (2015). \n                    https:\/\/doi.org\/10.1109\/TGRS.2014.2357078","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"1133_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Y Bengio, Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1\u2013127 (2009). \n                    https:\/\/doi.org\/10.1561\/2200000006","journal-title":"Found. Trends Mach. Learn."},{"key":"1133_CR8","doi-asserted-by":"publisher","first-page":"3626","DOI":"10.1109\/CVPR.2013.465","volume-title":"Preceedings of Computer Vision and Pattern Detection (CVPR), Portland","author":"P Sermanet","year":"2012","unstructured":"P Sermanet, K Kavukcuoglu, S Chintala, Y Lecun, in Preceedings of Computer Vision and Pattern Detection (CVPR), Portland. Pedestrian detection with unsupervised multi-stage feature learning (2012), pp. 3626\u20133633. \n                    https:\/\/doi.org\/10.1109\/CVPR.2013.465"},{"issue":"7","key":"1133_CR9","doi-asserted-by":"publisher","first-page":"3368","DOI":"10.1016\/j.eswa.2014.11.069","volume":"42","author":"H Yin","year":"2015","unstructured":"H Yin, X Jiao, Y Chai, B Fang, Scene classification based on single-layer SAE and SVM. Expert Syst. Appl. 42(7), 3368\u20133380 (2015). \n                    https:\/\/doi.org\/10.1016\/j.eswa.2014.11.069","journal-title":"Expert Syst. Appl."},{"key":"1133_CR10","doi-asserted-by":"publisher","unstructured":"Liu H, Taniguchi T, Takano T, Tanaka Y. Visualization of driving behavior using deep sparse autoencoder. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium, Dearborn: 1427-1434 (2014). doi: \n                    https:\/\/doi.org\/10.1109\/IVS.2014.6856506","DOI":"10.1109\/IVS.2014.6856506"},{"issue":"1","key":"1133_CR11","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/s13634-015-0222-1","volume":"2015","author":"Z Li","year":"2015","unstructured":"Z Li, Y Fan, W Liu, The effect of whitening transformation on pooling operations in convolutional autoencoders. EURASIP J. Adv. Signal Process. 2015(1), 37 (2015). \n                    https:\/\/doi.org\/10.1186\/s13634-015-0222-1","journal-title":"EURASIP J. Adv. Signal Process."},{"issue":"10","key":"1133_CR12","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.1080\/01431161.2016.1171928","volume":"37","author":"E Othman","year":"2016","unstructured":"E Othman, Y Bazi, N Alajlan, H Alhichri, F Melgani, Using convolutional features and a sparse autoencoder for land-use scene classification. Int. J. Remote Sens. 37(10), 2149\u20132167 (2016). \n                    https:\/\/doi.org\/10.1080\/01431161.2016.1171928","journal-title":"Int. J. Remote Sens."},{"key":"1133_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICMEW.2013.6618257","volume-title":"2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), San Jose, USA","author":"R Wang","year":"2013","unstructured":"R Wang, L Du, Z Yu, W Wan, in 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), San Jose, USA. Infrared and visible images fusion using compressed sensing based on average gradient (2013), pp. 1\u20134. \n                    https:\/\/doi.org\/10.1109\/ICMEW.2013.6618257"},{"key":"1133_CR14","unstructured":"AJ Bell, TJ Sejnowski, in Preceedings of the 10\n                           \n                    th\n                  \n                           Annual Conference on Neural Information Processing Systems (NIPS), Denver. Edges are the \u201cindependent components\u201d of natural scenes (1997), pp. 831\u2013837"},{"key":"1133_CR15","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1007\/978-3-642-42051-1_22","volume":"8228","author":"H Zhang","year":"2013","unstructured":"H Zhang, Z Yang, M G\u00f6nen, M Koskela, J Laaksonen, T Honkela, E Oja, Affective abstract image classification and retrieval using multiple kernel learning. Int. Conf. Neural Inf. Process. 8228, 166\u2013175 (2013). \n                    https:\/\/doi.org\/10.1007\/978-3-642-42051-1_22","journal-title":"Int. Conf. Neural Inf. Process."},{"key":"1133_CR16","doi-asserted-by":"publisher","unstructured":"He Zhang, Eimontas Augilius, Timo Honkela, Jorma Laaksonen, Hannes Gamper, and Henok Alene. Analyzing emotional semantics of abstract art using low-level image features. In Proceedings of 10th International Symposium on Intelligent Data Analysis (IDA 2011). Springer, 2011. \n                    https:\/\/doi.org\/10.1007\/978-3-642-24800-9_38","DOI":"10.1007\/978-3-642-24800-9_38"},{"key":"1133_CR17","doi-asserted-by":"publisher","unstructured":"DC Ciresan, U Meier, J Masci, LM Gambardella, J Schmidhuber, in Proceedings of the 22\n                           \n                    nd\n                  \n                           International Joint Conference on Artificial Intelligence, Barcelona. Flexible, high performance convolutional neural networks for image classification (2011), pp. 1237\u20131242. \n                    https:\/\/doi.org\/10.5591\/978-1-57735-516-8\/IJCAI11-210","DOI":"10.5591\/978-1-57735-516-8\/IJCAI11-210"},{"issue":"25","key":"1133_CR18","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1049\/el.2014.2526","volume":"50","author":"R Zeng","year":"2014","unstructured":"R Zeng, J Wu, Z Shao, L Senhadji, S Huazhong, Quaternion softmax classifier. Electron. Lett. 50(25), 1929\u20131930 (2014). \n                    https:\/\/doi.org\/10.1049\/el.2014.2526","journal-title":"Electron. Lett."},{"key":"1133_CR19","unstructured":"A Coates, H Lee, AY Ng, in Proceedings of the 14\n                           \n                    th\n                  \n                           International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, USA. An analysis of single-layer networks in unsupervised feature learning (2011), pp. 215\u2013223"},{"issue":"2","key":"1133_CR20","doi-asserted-by":"publisher","first-page":"384","DOI":"10.13328\/j.cnki.jos.005061","volume":"28","author":"WX Mao","year":"2017","unstructured":"WX Mao, ZM Cai, L Tong, Malware detection method based on active learning. J. Softw. 28(2), 384\u2013397 (2017). \n                    https:\/\/doi.org\/10.13328\/j.cnki.jos.005061","journal-title":"J. Softw."}],"container-title":["EURASIP Journal on Wireless Communications and Networking"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-018-1133-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13638-018-1133-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-018-1133-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,20]],"date-time":"2019-05-20T19:07:19Z","timestamp":1558379239000},"score":1,"resource":{"primary":{"URL":"https:\/\/jwcn-eurasipjournals.springeropen.com\/articles\/10.1186\/s13638-018-1133-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,21]]},"references-count":20,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["1133"],"URL":"https:\/\/doi.org\/10.1186\/s13638-018-1133-2","relation":{},"ISSN":["1687-1499"],"issn-type":[{"type":"electronic","value":"1687-1499"}],"subject":[],"published":{"date-parts":[[2018,5,21]]},"assertion":[{"value":"7 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"127"}}