{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:01:22Z","timestamp":1730264482622,"version":"3.28.0"},"reference-count":16,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7]]},"DOI":"10.1109\/igarss.2017.8127435","type":"proceedings-article","created":{"date-parts":[[2017,12,13]],"date-time":"2017-12-13T19:00:14Z","timestamp":1513191614000},"page":"2243-2246","source":"Crossref","is-referenced-by-count":23,"title":["Real-time scene understanding for UAV imagery based on deep convolutional neural networks"],"prefix":"10.1109","author":[{"given":"Clay","family":"Sheppard","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam","family":"Rahnemoonfar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"year":"2015","author":"ioffe","journal-title":"Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift","key":"ref11"},{"year":"2014","author":"kingma","journal-title":"Adam A method for stochastic optimization","key":"ref12"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1007\/s10479-005-5724-z"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/ICASSP.2013.6639346"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.2514\/6.2016-0745"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1016\/j.patcog.2004.03.009"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1016\/0734-189X(88)90016-3"},{"key":"ref3","first-page":"1193","article-title":"A light-weight multispectral sensor for micro UAV&#x2014;Opportunities for very high resolution airborne remote sensing","volume":"37","author":"nebiker","year":"2008","journal-title":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1109\/TGRS.2012.2200689"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/ISCIS.2008.4717854"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1145\/2072298.2072344"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1016\/j.isprsjprs.2015.06.007"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1016\/j.enggeo.2011.03.012"},{"key":"ref1","volume":"33","author":"valavanis","year":"2008","journal-title":"Advances in Unmanned Aerial Vehicles State of the Art and the Road to Autonomy"},{"year":"2016","author":"dumoulin","journal-title":"A guide to convolution arithmetic for deep learning","key":"ref9"}],"event":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","start":{"date-parts":[[2017,7,23]]},"location":"Fort Worth, TX","end":{"date-parts":[[2017,7,28]]}},"container-title":["2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8118204\/8126808\/08127435.pdf?arnumber=8127435","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,1,17]],"date-time":"2018-01-17T23:12:58Z","timestamp":1516230778000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8127435\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7]]},"references-count":16,"URL":"https:\/\/doi.org\/10.1109\/igarss.2017.8127435","relation":{},"subject":[],"published":{"date-parts":[[2017,7]]}}}