[1] 张迪飞, 张金锁, 姚克明, 等. 基于SVM分类的红外舰船目标识别[J]. 红外与激光工程, 2016, 45(1): 0104004.

    张迪飞, 张金锁, 姚克明, 等. 基于SVM分类的红外舰船目标识别[J]. 红外与激光工程, 2016, 45(1): 0104004.

    Zhang Difei, Zhang Jinsuo, Yao Keming, et al. Infrared ship-target recognition based on SVM classification[J]. Infrared and Laser Engineering, 2016, 45(1): 0104004.

    Zhang Difei, Zhang Jinsuo, Yao Keming, et al. Infrared ship-target recognition based on SVM classification[J]. Infrared and Laser Engineering, 2016, 45(1): 0104004.

[2] RamananD, ZhuX. Face detection, pose estimation, and landmark localization in the wild[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012: 2879- 2886.

    RamananD, ZhuX. Face detection, pose estimation, and landmark localization in the wild[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012: 2879- 2886.

[3] Wojek C, Dollár P, Schiele B, et al. Pedestrian detection: an evaluation of the state of the art[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 743-761.

    Wojek C, Dollár P, Schiele B, et al. Pedestrian detection: an evaluation of the state of the art[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 743-761.

[4] Feineigle PA, Morris DD, Snyder FD. Ship recognition using optical imagery for harbor surveillance[C]. Proceedings of AUVSI Unmanned Systems North America Conference, 2007: 249- 263.

    Feineigle PA, Morris DD, Snyder FD. Ship recognition using optical imagery for harbor surveillance[C]. Proceedings of AUVSI Unmanned Systems North America Conference, 2007: 249- 263.

[5] Sánchez J, Perronnin F, Mensink T, et al. Image classification with the fisher vector: theory and practice[J]. International Journal of Computer Vision, 2013, 105(3): 222-245.

    Sánchez J, Perronnin F, Mensink T, et al. Image classification with the fisher vector: theory and practice[J]. International Journal of Computer Vision, 2013, 105(3): 222-245.

[6] Smeelen M A. Schwering P B W, Toet A, et al. Semi-hidden target recognition in gated viewer images fused with thermal IR images[J]. Information Fusion, 2014, 18: 131-147.

    Smeelen M A. Schwering P B W, Toet A, et al. Semi-hidden target recognition in gated viewer images fused with thermal IR images[J]. Information Fusion, 2014, 18: 131-147.

[7] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251.

    周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251.

    Zhou Feiyan, Jin Linpeng, Dong Jun. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251.

    Zhou Feiyan, Jin Linpeng, Dong Jun. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251.

[8] KrizhevskyA, SutskeverI, Hinton GE. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems, 2012: 1097- 1105.

    KrizhevskyA, SutskeverI, Hinton GE. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems, 2012: 1097- 1105.

[9] 刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016, 36(4): 0428001.

    刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016, 36(4): 0428001.

    Liu Dawei, Han Ling, Han Xiaoyong. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

    Liu Dawei, Han Ling, Han Xiaoyong. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

[10] HeK, ZhangX, RenS, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770- 778.

    HeK, ZhangX, RenS, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770- 778.

[11] RenS, HeK, GirshickR, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]. Advances in Neural Information Processing Systems, 2015: 91- 99.

    RenS, HeK, GirshickR, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]. Advances in Neural Information Processing Systems, 2015: 91- 99.

[12] RedmonJ, DivvalaS, GirshickR, et al. You only look once: unified, real-time objectdetection[J/OL]. ( 2016-05-09)[2017-01-05] https:∥arxiv. org/abs/1506. 02640.

    RedmonJ, DivvalaS, GirshickR, et al. You only look once: unified, real-time objectdetection[J/OL]. ( 2016-05-09)[2017-01-05] https:∥arxiv. org/abs/1506. 02640.

[13] KuenJ, WangZ, WangG. Recurrent attentional networks for saliency detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 3668- 3677.

    KuenJ, WangZ, WangG. Recurrent attentional networks for saliency detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 3668- 3677.

[14] BousetouaneF, MorrisB. Fast CNN surveillance pipeline for fine-grained vessel classification and detection in maritime scenarios[C]. IEEE International Conference on Advanced Video and Signal Based Surveillance, 2016: 242- 248.

    BousetouaneF, MorrisB. Fast CNN surveillance pipeline for fine-grained vessel classification and detection in maritime scenarios[C]. IEEE International Conference on Advanced Video and Signal Based Surveillance, 2016: 242- 248.

[15] Zhang MM, ChoiJ, DaniilidisK, et al. VAIS: a dataset for recognizing maritime imagery in the visible and infrared spectrums[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015: 10- 16.

    Zhang MM, ChoiJ, DaniilidisK, et al. VAIS: a dataset for recognizing maritime imagery in the visible and infrared spectrums[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015: 10- 16.

[16] NairV, Hinton GE. Rectified linear units improve restricted Boltzmann machines[C]. Proceedings of the 27 th International Conference on Machine Learning , 2010: 807- 814.

    NairV, Hinton GE. Rectified linear units improve restricted Boltzmann machines[C]. Proceedings of the 27 th International Conference on Machine Learning , 2010: 807- 814.

[17] ZhangY, WuJ, CaiJ. Compact representation for image classification: to choose or to compress?[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 907- 914.

    ZhangY, WuJ, CaiJ. Compact representation for image classification: to choose or to compress?[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 907- 914.

[18] YeP, KumarJ, DoermannD. Beyond human opinion scores: blind image quality assessment based on synthetic scores[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 4241- 4248.

    YeP, KumarJ, DoermannD. Beyond human opinion scores: blind image quality assessment based on synthetic scores[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 4241- 4248.

[19] Xue W, Zhang L, Mou X, et al. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index[J]. IEEE Transactions on Image Processing, 2014, 23(2): 684-695.

    Xue W, Zhang L, Mou X, et al. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index[J]. IEEE Transactions on Image Processing, 2014, 23(2): 684-695.

[20] Sheikh H R. Bovik A C, de Veciana G. An information fidelity criterion for image quality assessment using natural scene statistics[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2117-2128.

    Sheikh H R. Bovik A C, de Veciana G. An information fidelity criterion for image quality assessment using natural scene statistics[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2117-2128.

[21] Zhang L, Zhang L, Mou X, et al. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.

    Zhang L, Zhang L, Mou X, et al. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.

[22] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

    Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.