{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:31:12Z","timestamp":1760369472048},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2018,6,30]],"date-time":"2018-06-30T00:00:00Z","timestamp":1530316800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Science Fund for Distinguished Young Scholars of China","award":["60902067"],"award-info":[{"award-number":["60902067"]}]},{"name":"the key Science-Technology Project of Jilin Province","award":["11ZDGG001"],"award-info":[{"award-number":["11ZDGG001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s11063-018-9878-5","type":"journal-article","created":{"date-parts":[[2018,6,30]],"date-time":"2018-06-30T09:38:44Z","timestamp":1530351524000},"page":"1369-1379","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images"],"prefix":"10.1007","volume":"49","author":[{"given":"Peng","family":"Ding","sequence":"first","affiliation":[]},{"given":"Ye","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Xu-ling","family":"Chang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,6,30]]},"reference":[{"issue":"12","key":"9878_CR1","doi-asserted-by":"publisher","first-page":"5659","DOI":"10.1109\/TIP.2015.2487860","volume":"24","author":"C Hong","year":"2015","unstructured":"Hong C, Yu J, Wan J, Tao D, Wang M (2015) multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659\u20135670","journal-title":"IEEE Trans Image Process"},{"key":"9878_CR2","doi-asserted-by":"crossref","unstructured":"Hong C, Yu J, Chen X (2013) Image-based 3D human pose recovery with locality sensitive sparse retrieval. In: Systems, man and cybernetics, pp 2103\u20132108","DOI":"10.1109\/SMC.2013.360"},{"issue":"7553","key":"9878_CR3","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444. \n                    https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"9878_CR4","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117. \n                    https:\/\/doi.org\/10.1016\/j.neunet.2014.09.003","journal-title":"Neural Netw"},{"issue":"11","key":"9878_CR5","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"1","key":"9878_CR6","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"9878_CR7","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: AISTATS, pp 315\u2013323"},{"key":"9878_CR8","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich (2015) A Going deeper with convolutions. In: Computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9878_CR9","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: Computer vision and pattern recognition, pp 770\u2013778"},{"key":"9878_CR10","unstructured":"Zisserman KSAA (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations (ICLR)"},{"issue":"10","key":"9878_CR11","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1109\/LGRS.2014.2309695","volume":"11","author":"X Chen","year":"2014","unstructured":"Chen X, Xiang S, Liu CL, Pan CH (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11(10):1797\u20131801. \n                    https:\/\/doi.org\/10.1109\/LGRS.2014.2309695","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"#cr-split#-9878_CR12.1","doi-asserted-by":"crossref","unstructured":"Zhang QJ, Xu JL, Xu L, Guo HF (2016) Deep convolutional neural networks for forest fire detection. In: Kim YH","DOI":"10.2991\/ifmeita-16.2016.105"},{"key":"#cr-split#-9878_CR12.2","unstructured":"(ed) Proceedings of the 2016 international forum on management, education and information technology application, vol 47. Advances in social science education and humanities research, pp 568-575. Atlantis Press, Paris"},{"key":"9878_CR13","doi-asserted-by":"crossref","unstructured":"Hafemann LG, Oliveira LS, Cavalin PR (2014) Forest species recognition using deep convolutional neural networks. In: International conference on pattern recognition, pp 1103\u20131107","DOI":"10.1109\/ICPR.2014.199"},{"key":"9878_CR14","unstructured":"Castelluccio M, Poggi G, Sansone C, Verdoliva L (2015) Land use classification in remote sensing images by convolutional neural networks. In: Computer science"},{"key":"9878_CR15","doi-asserted-by":"crossref","unstructured":"Lecun Y, Kavukcuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In: International symposium on circuits and systems, pp 253\u2013256","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"9878_CR16","doi-asserted-by":"crossref","unstructured":"Scherer D, Muller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: International conference on artificial neural networks","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"9878_CR17","doi-asserted-by":"publisher","unstructured":"Girshick R, Donahue J, Darrell T, Malik J, Ieee (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE conference on computer vision and pattern recognition, pp 580\u2013587. IEEE, New York. \n                    https:\/\/doi.org\/10.1109\/cvpr.2014.81","DOI":"10.1109\/cvpr.2014.81"},{"key":"9878_CR18","doi-asserted-by":"publisher","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision-ECCV 2014: 13th European conference, Zurich, Switzerland, September 6\u201312, 2014, proceedings, part I. Springer, Cham, pp 818\u2013833. \n                    https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"9878_CR19","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. \n                    arxiv:1312.4400"},{"key":"9878_CR20","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. \n                    arXiv:1602.07360"},{"key":"9878_CR21","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: ACM multimedia, pp 675\u2013678","DOI":"10.1145\/2647868.2654889"},{"key":"9878_CR22","doi-asserted-by":"publisher","unstructured":"Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Lechevallier Y, Saporta G (eds) Proceedings of COMPSTAT\u20192010: 19th International Conference on computational statistics, Paris France, August 22\u201327, 2010 Keynote, invited and contributed papers, pp 177\u2013186. Physica-Verlag HD, Heidelberg. \n                    https:\/\/doi.org\/10.1007\/978-3-7908-2604-3_16","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"9878_CR23","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/978-3-642-35289-8_25","volume-title":"Neural networks: tricks of the trade","author":"L Bottou","year":"2012","unstructured":"Bottou L (2012) Stochastic gradient descent tricks. In: Montavon G, Orr GB, M\u00fcller K-R (eds) Neural networks: tricks of the trade, 2nd edn. Springer, Berlin, pp 421\u2013436. \n                    https:\/\/doi.org\/10.1007\/978-3-642-35289-8_25","edition":"2"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-018-9878-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-018-9878-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-018-9878-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,29]],"date-time":"2019-06-29T23:13:18Z","timestamp":1561849998000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-018-9878-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,30]]},"references-count":24,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["9878"],"URL":"https:\/\/doi.org\/10.1007\/s11063-018-9878-5","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,30]]},"assertion":[{"value":"30 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}