[1] J E BALL, D T ANDERSON, C S CHAN. A comprehensive survey of deep learning in remote sensing:theories, tools and challenges for the community. Journal of Applied Remote Sensing, 11, 042609(2017).

[2] A KRIZHEVSKY, I SUTSKEVER, G HINTON. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1106-1114(2012).

[3] O RUSSAKOVSKY, J DENG, H SU. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115, 211-252(2015).

[4] R GIRSHICK. Fast R-CNN. Computer Science, 6, 1440-1448(2015).

[5] S REN, K HE, R GIRSHICK. Faster R-CNN:towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39, 1137-1149(2017).

[6] [6] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, realtime object detection[C]. Conference on Computer Vision Pattern Recognition, 2016, 6: 779788.https:www.researchgate.publication278049038_You_Only_Look_Once_Unified_RealTime_Object_Detection

[7] [7] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detect[C]. European Conference on Computer Vision, 2016, 6: 2127.https:link.springer.comchapter10.10079783319464480_2

[8] [8] HEI L, JIA D.Cner: detecting objects as paired keypoints[C]. European Conference on Computer Vision, 2018, 9: 734750.

[9] [9] REDMON J, FARHADI A. YOLO9000: Better, faster, stronger[C]. Conference on Computer Vision Pattern Recognition, 2017, 7: 65176525.

[10] [10] REDMON J, FARHADI A. YOLOv3: An incremental improvement[J]. arXiv: 1804.02767, 2018.https:arxiv.gpdf1804.02767.pdf

[11] [11] FU C, LIU W, RANGAA, et al. DSSD: Deconvolutional single shot detect[J]. arXiv: 1701.06659, 2017.https:www.researchgate.publication312759848_DSSD_Deconvolutional_Single_Shot_Detect

[12] J LENG, Y LIU. An enhanced SSD with feature fusion and visual reasoning for object detection. Neural Computing and Applications, 1-10(2018).

[13] [13] CAO G, XIE X, YANG W, et al. Featurefused ssd: fast detection f small objects[J]. arXiv: 1709.05054, 2017.

[16] [16] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional wks[C]. Conference on Computer Vision Pattern Recognition, 2017, 7: 22612269.

[17] [17] LIN T Y, DOLLAR P, GIRSHICK R, et al. Feature pyra wks f object detection[C]. Conference on Computer Vision Pattern Recognition, 2017, 7: 936944.https:www.researchgate.publication311573567_Feature_Pyra_wks_f_Object_Detection

[20] Z CHEN, T ZHANG, C OUYANG. End-to-end airplane detection using transfer learning in remote sensing images. Remote Sensing, 10, 139(2018).

[21] [21] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]. Conference on Computer Vision Pattern Recognition, 2015, 6: 19.https:www.researchgate.publication265787949_Going_Deeper_with_Convolutions

[22] [22] HE K, ZHANG X, REN S, et al. Deep residual learning f image recognition[C]. Conference on Computer Vision Pattern Recognition, 2016, 770778.https:www.researchgate.publication307964703_Deep_Residual_Learning

[23] [23] CHEN Y, LI J, XIAO H, et al.Dual path wks[J]. arXiv: 1707.01629, 2017.https:www.researchgate.publication318255371_Dual_Path_wks

[24] S QIU, G WEN, Z DENG. Accurate non-maximum suppression for object detection in high-resolution remote sensing images. Remote Sensing Letters, 9, 238-247(2018).

[25] [25] HUANG G, LIU Z, LAURENS V D M, et al. Densely connected convolutional wks[C]. Conference on Computer Vision Pattern Recognition, 2017, 7: 47004708.https:www.researchgate.publication319770123_Densely_Connected_Convolutional_wksev=prf_high

[26] T Y LIN, P GOYAL, R GIRSHICK. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis & Machine Intelligence, 99, 2999-3007(2017).

[27] M EVERINGHAM, S M A ESLAMI, L V GOOL. The pascal visual object classes challenge:a retrospective. International Journal of Computer Vision, 111, 98-136(2015).

[28] G CHENG, P ZHOU, J HAN. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54, 7405-7415(2016).

[29] G.S XIA, J HU, F HU. Aid:A benchmark dataset for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 3965-3981(2017).