[1] 郭呈呈, 于凤芹, 陈莹. 基于卷积神经网络特征和改进超像素匹配的图像语义分割[J]. 激光与光电子学进展, 2018, 55(8): 081005.

    Guo C C, Yu F Q, Chen Y. Image semantic segmentation based on convolutional neural network feature and improved superpixel matching[J]. Laser & Optoelectronics Progress, 2018, 55(8): 081005.

[2] LeCun Y, Bottou L, Bengio Y, et al. . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

[3] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[4] DengJ, DongW, SocherR, et al. ImageNet: a large-scale hierarchical image database[C]∥2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE, 2009: 248- 255.

[5] LongJ, ShelhamerE, DarrellT. Fully convolutional networks for semantic segmentation[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 3431- 3440.

[6] 方旭, 王光辉, 杨化超, 等. 结合均值漂移分割与全卷积神经网络的高分辨遥感影像分类[J]. 激光与光电子学进展, 2018, 55(2): 022802.

    Fang X, Wang G H, Yang H C, et al. High resolution remote sensing image classification combining with mean-shift segmentation and fully convolution neural network[J]. Laser & Optoelectronics Progress, 2018, 55(2): 022802.

[7] 王琳, 刘强. 基于局部特征的多目标图像分割算法[J]. 激光与光电子学进展, 2018, 55(6): 061002.

    Wang L, Liu Q. A multi-object image segmentation algorithm based on local feature[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061002.

[8] Zhao HS, Shi JP, Qi XJ, et al. Pyramid scene parsing network[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 6230- 6239.

[9] Chen LC, PapandreouG, KokkinosI, et al. Semantic image segmentation with deep convolutional nets and fully connectedCRFs[J/OL]. ( 2016-06-07)[2019-03-30]. https:∥arxiv.org/abs/1412. 7062.

[10] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.

[11] Chen LC, PapandreouG, SchroffF, et al. Rethinking atrous convolution for semantic image segmentation[J/OL]. ( 2017-12-05)[2019-03-30]. https∥arxiv.org/abs/1706. 05587.

[12] Chen LC, Zhu YK, PapandreouG, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M] ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 2018, 11211: 833- 851.

[13] Yang MK, YuK, ZhangC, et al. DenseASPP for semantic segmentation in street scenes[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 3684- 3692.

[14] PengC, Zhang XY, YuG, et al. Large kernel matters: improve semantic segmentation by global convolutional network[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 1743- 1751.

[15] Wang XL, GirshickR, GuptaA, et al. Non-local neural networks[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 7794- 7803.

[16] FuJ, LiuJ, Tian HJ, et al. Dual attention network for scene segmentation[C]∥The IEEE Conference on Computer Vision and Pattern Recognition, June 16-20, 2019, Long Beach, CA, USA. New York: IEEE, 2019: 3146- 3154.

[17] Huang ZL, Wang XG, Huang LC, et al. CCNet: criss-cross attention for semantic segmentation [J/OL]. ( 2018-11-28) [2019-03-30]. https∥arxiv.org/abs/1811. 11721.

[18] LiuW, AnguelovD, ErhanD, et al. SSD: single shot MultiBox detector[M] ∥Leibe B, Matas J, Sebe N, et al. Computer vision - ECCV 2016. Lecture notes in computer science. Cham: Springer, 2016, 9905: 21- 37.

[19] Cai ZW, Fan QF, Feris RS, et al. A unified multi-scale deep convolutional neural network for fast object detection[M] ∥Leibe B, Matas J, Sebe N, et al. Computer vision - ECCV 2016. Lecture notes in computer science. Cham: Springer, 2016, 9908: 354- 370.

[20] Lin GS, MilanA, Shen CH, et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 5168- 5177.

[21] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770- 778.

[22] Lin TY, DollárP, GirshickR, et al. Feature pyramid networks for object detection[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 936- 944.

[23] CordtsM, OmranM, RamosS, et al. The cityscapes dataset[C]∥CVPR Workshop on the Future of Datasets in Vision, June 7-12, 2015, Boston, Massachusetts. New York: IEEE, 2015.

[24] CordtsM, OmranM, RamosS, et al. The cityscapes dataset for semantic urban scene understanding[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 3213- 3223.

[25] ZhangH, DanaK, Shi JP, et al. Context encoding for semantic segmentation[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 7151- 7160.