[1] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[2] GU J, WANG Z, KUEN J, et al. Recent advances in convolutional neural networks[DB/OL]. [2017-05-02]. http: //www. researchgate. net/publication/2880601.
[3] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012: 1097-1105.
[4] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
[5] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[DB/OL]. [2017-05-01]. http: //arxiv. org/pdf/1512. 03385v/pdf.
[7] WEYAND T, KOSTRIKOV I, PHILBIN J. Planet-photo geolocation with convolutional neural networks[C]//European Conference on Computer Vision, Springer International Publishing, 2016: 37-55.
[9] DONAHUE J, JIA Y Q, VINYALS O, et al. DeCAF: a deep convolutional activation feature for generic visual recognition[C]//ICML, 2014, 32: 647-655.
[10] WANG Z H, WANG X X, WANG G. Learning fine-grained features via a CNN tree for large-scale classification[DB/OL]. [2017-05-01]. http: //www. researchgate. net/publication/2840970.
[11] WORKMAN S, SOUVENIR R, JACOBS N. Wide-area image geolocalization with aerial reference imagery[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 3961-3969.
[12] MARMANIS D, DATCU M, ESCH T, et al. Deep learning earth observation classification using ImageNet pretrained networks[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(1): 105-109.