[1] 王中挺, 陈良富, 厉青, 等. 灰霾组分的多角度偏振反射特性模拟[J]. 光学学报, 2015, 35(9): 0901002.

    王中挺, 陈良富, 厉青, 等. 灰霾组分的多角度偏振反射特性模拟[J]. 光学学报, 2015, 35(9): 0901002.

    Wang Zhongting, Chen Liangfu, Li Qing, et al. Simulation of multi-angle polarized reflectance of haze[J]. Acta Optica Sinica, 2015, 35(9): 0901002.

    Wang Zhongting, Chen Liangfu, Li Qing, et al. Simulation of multi-angle polarized reflectance of haze[J]. Acta Optica Sinica, 2015, 35(9): 0901002.

[2] 刘建国, 桂华侨, 谢品华, 等. 大气灰霾监测技术研究进展[J]. 大气与环境光学学报, 2015, 10(2): 93-101.

    刘建国, 桂华侨, 谢品华, 等. 大气灰霾监测技术研究进展[J]. 大气与环境光学学报, 2015, 10(2): 93-101.

    Liu Jianguo, Gui Huaqiao, Xie Pinhua, et al. Recent progress of atmospheric haze monitoring technology[J]. Journal of Atmospheric and Environmental Optics, 2015, 10(2): 93-101.

    Liu Jianguo, Gui Huaqiao, Xie Pinhua, et al. Recent progress of atmospheric haze monitoring technology[J]. Journal of Atmospheric and Environmental Optics, 2015, 10(2): 93-101.

[3] Lee K H, Kim Y J, Kim M J. Characteristics of aerosol observed during two severe haze events over Korea in June and October 2004[J]. Atmospheric Environment, 2006, 40(27): 5146-5155.

    Lee K H, Kim Y J, Kim M J. Characteristics of aerosol observed during two severe haze events over Korea in June and October 2004[J]. Atmospheric Environment, 2006, 40(27): 5146-5155.

[4] GhauriB. Estimating area covered by Haze and fog in Pakistan and India during winters[C]. IEEE Conference on Geoscience and Remote Sensing Symposium (IGARSS), 2016: 16445492.

    GhauriB. Estimating area covered by Haze and fog in Pakistan and India during winters[C]. IEEE Conference on Geoscience and Remote Sensing Symposium (IGARSS), 2016: 16445492.

[5] 戴羊羊, 李成范, 周时强, 等. 基于遥感的上海地区雾霾监测研究[J]. 测绘工程, 2015(12): 29-32.

    戴羊羊, 李成范, 周时强, 等. 基于遥感的上海地区雾霾监测研究[J]. 测绘工程, 2015(12): 29-32.

    Dai Yangyang, Li Chengfan, Zhou Shiqiang, et al. Haze monitoring of Shanghai area based on remote sensing[J]. Engineering of Surveying and Mapping, 2015(12): 29-32.

    Dai Yangyang, Li Chengfan, Zhou Shiqiang, et al. Haze monitoring of Shanghai area based on remote sensing[J]. Engineering of Surveying and Mapping, 2015(12): 29-32.

[6] 王中挺, 厉青, 李莘莘, 等. 基于环境一号卫星的霾监测应用[J]. 光谱学与光谱分析, 2012, 32(3): 775-780.

    王中挺, 厉青, 李莘莘, 等. 基于环境一号卫星的霾监测应用[J]. 光谱学与光谱分析, 2012, 32(3): 775-780.

    Wang Zhongting, Li Qing, Li Shenshen, et al. The monitoring of haze from HJ-1[J]. Spectroscopy and Spectral Analysis, 2012, 32(3): 775-780.

    Wang Zhongting, Li Qing, Li Shenshen, et al. The monitoring of haze from HJ-1[J]. Spectroscopy and Spectral Analysis, 2012, 32(3): 775-780.

[7] . NOAA/AVHRR 卫星资料的北京地区霾识别研究[J]. 气象, 2014, 40(5): 619-627.

    . NOAA/AVHRR 卫星资料的北京地区霾识别研究[J]. 气象, 2014, 40(5): 619-627.

    Liu Yonghong. Research on haze identification in Beijing based on NOAA/AVHRR satellite data[J]. Meteorological Monthly, 2014, 40(5): 619-627.

    Liu Yonghong. Research on haze identification in Beijing based on NOAA/AVHRR satellite data[J]. Meteorological Monthly, 2014, 40(5): 619-627.

[8] 牛志春, 姜晟, 李旭文, 等. 江苏省霾污染遥感监测业务化运行研究[J]. 环境监控与预警, 2014, 6(5): 15-18.

    牛志春, 姜晟, 李旭文, 等. 江苏省霾污染遥感监测业务化运行研究[J]. 环境监控与预警, 2014, 6(5): 15-18.

    Niu Zhichun, Jiang Sheng, Li Xuwen, et al. The remote sensing monitoring operational system of haze pollution in Jiangsu province[J]. Environmental Monitoring & Forewarning, 2014, 6(5): 15-18.

    Niu Zhichun, Jiang Sheng, Li Xuwen, et al. The remote sensing monitoring operational system of haze pollution in Jiangsu province[J]. Environmental Monitoring & Forewarning, 2014, 6(5): 15-18.

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

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

[10] GirshickR, DonahueJ, DarrellT, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, 2014: 580- 587.

    GirshickR, DonahueJ, DarrellT, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, 2014: 580- 587.

[11] 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.

[12] 尹宝才, 王文通, 王立春. 深度学习研究综述[J]. 北京工业大学学报, 2015, 1(1): 48-59.

    尹宝才, 王文通, 王立春. 深度学习研究综述[J]. 北京工业大学学报, 2015, 1(1): 48-59.

    Yin Baocai, Wang Wentong, Wang Lichun. Review of deep learning[J]. Journal of Beijing University of Technology, 2015, 1(1): 48-59.

    Yin Baocai, Wang Wentong, Wang Lichun. Review of deep learning[J]. Journal of Beijing University of Technology, 2015, 1(1): 48-59.

[13] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770- 778.

    He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770- 778.

[14] 徐文斌, 郑小兵, 易维宁. 基于超光谱成像仪Hyperion的交叉定标方法[J]. 光学学报, 2013, 33(5): 0528002.

    徐文斌, 郑小兵, 易维宁. 基于超光谱成像仪Hyperion的交叉定标方法[J]. 光学学报, 2013, 33(5): 0528002.

    Xu Wenbin, Zheng Xiaobing, Yi Weining. Cross-calibration method based on hyperspectral imager hyperion[J]. Acta Optica Sinica, 2013, 33(5): 0528002.

    Xu Wenbin, Zheng Xiaobing, Yi Weining. Cross-calibration method based on hyperspectral imager hyperion[J]. Acta Optica Sinica, 2013, 33(5): 0528002.

[15] IoffeS, SzegedyC. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. Proceedings of the 32 nd International Conference on Machine Learning , 2015, 37: 448- 456.

    IoffeS, SzegedyC. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. Proceedings of the 32 nd International Conference on Machine Learning , 2015, 37: 448- 456.

[16] Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4): 212-223.

    Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4): 212-223.

[17] GlorotX, BordesA, BengioY. Deep sparse rectifier neural networks[C]. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011, 15: 315- 323.

    GlorotX, BordesA, BengioY. Deep sparse rectifier neural networks[C]. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011, 15: 315- 323.

[18] BottouL. Large-scale machine learning with stochastic gradient descent[C]. 19 th International Conference on Computational Statistics , 2010: 177- 186.

    BottouL. Large-scale machine learning with stochastic gradient descent[C]. 19 th International Conference on Computational Statistics , 2010: 177- 186.