[1] 马岩, 张明松, 杨春梅, 等. 森林火灾的危害及重要灭火手段的分析[J]. 森林工程, 2013, 29(6):25−27.

Ma Y, Zhang M S, Yang C M, et al. Analysis of forest fire hazards and important means of fighting fires[J]. Forest Engineering, 2013, 29(6): 25−27.
[2] 林燕, 岩糯香. 森林防火现状及火灾控制措施[J]. 现代园艺, 2017(2):223.

Lin Y, Yan N X. Current situation of forest fire prevention and fire control measures[J]. Xiandai Horticulture, 2017(2): 223.
[3] 邹全程, 王丽娜. 我国县(区)级单位森林防火能力建设研究: 以威海市环翠区为例[J]. 林业调查规划, 2017, 42(6):33−37.

Zou Q C, Wang L N. Study on the capacity construction of forest fire prevention of counties (districts) in China: a case study of Huancui District, Weihai City[J]. Forest Inventory and Planning, 2017, 42(6): 33−37.
[4] 孙立研, 刘美玲, 周礼祥, 等. 基于气象因子深度学习的森林火灾预测方法[J]. 林业工程学报, 2019, 4(3):132−136.

Sun L Y, Liu M L, Zhou L X, et al. Research on forest fire prediction method based on deep learning[J]. Journal of Forestry Engineering, 2019, 4(3): 132−136.
[5] 张军阳, 王慧丽, 郭阳, 等. 深度学习相关研究综述[J]. 计算机应用研究, 2018, 35(7):1921−1928, 1936.

Zhang J Y, Wang H L, Guo Y, et al. Review of deep learning[J]. Application Research of Computers, 2018, 35(7): 1921−1928, 1936.
[6] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521: 436−444. doi:  10.1038/nature14539.
[7] 李卫. 深度学习在图像识别中的研究及应用[D]. 武汉: 武汉理工大学, 2014.

Li W. Research and application of deep learning in image recognition[D]. Wuhan: Wuhan University of Technology, 2014.
[8] 梁万杰, 曹宏鑫. 基于卷积神经网络的水稻虫害识别[J]. 江苏农业科学, 2017, 45(20):241−243, 253.

Liang W J, Cao H X. Rice pest identification based on convolutional neural network[J]. Jiangsu Agricultural Sciences, 2017, 45(20): 241−243, 253.
[9] Mohanty S P, Hughes D P, Salathé M. Using deep learning for image-based plant disease detection[J]. Frontiers in Plant Science, 2016, 7: 1−10. doi:  10.3389/fpls.2016.01419.
[10] 刘恒旭, 刘凯. 森林防火信息化建设技术问题与措施[J]. 科学技术创新, 2017(1):280.

Liu H X, Liu K. Technical problems and measures for forest fire prevention information construction[J]. Science and Technology Innovation, 2017(1): 280.
[11] 傅天驹, 郑嫦娥, 田野, 等. 复杂背景下基于深度卷积神经网络的森林火灾识别[J]. 计算机与现代化, 2016(3):52−57.

Fu T J, Zheng C E, Tian Y, et al. Forest fire recognition based on deep convolutional neural network under complex background[J]. Computer and Modernization, 2016(3): 52−57.
[12] 赵亚琴. 基于模糊神经网络的火灾识别算法[J]. 计算机仿真, 2015, 32(2):369−373.

Zhao Y Q. Forest fire recognition algorithm based on fuzzy nerual network[J]. Computer Simulation, 2015, 32(2): 369−373.
[13] 李诚, 唐李洋, 潘李伟. 城镇森林交界域视频烟雾检测算法[J]. 计算机工程, 2018, 44(1):258−262.

Li C, Tang L Y, Pan L W. Video smoke detection algorithm for wildland-urban interface[J]. Computer Engineering, 2018, 44(1): 258−262.
[14] 卢柯楠, 胡晓惠, 李小涛. 安防大数据技术与应用:基于多光谱大数据分析的智能森林火警检测[J]. 中国安全防范认证, 2017(3):10−15.

Lu K N, Hu X H, Li X T. Security big data technology and application: intelligent forest fire alarm detection based on multispectral big data analysis[J]. China Security Protection Certification, 2017(3): 10−15.
[15] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C/OL]//Proceedings of the 32nd International Conference on Machine Learning. 2015: 448−456 [2019−05−06]. https://arxiv.org/pdf/1502.03167.pdf.
[16] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2261−2269.
[17] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313: 504−507. doi:  10.1126/science.1127647.
[18] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 2818−2826.
[19] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision & Pattern Recognition. Las Vegas: IEEE, 2016: 770−778.
[20] Liu B Y, Wang M, Foroosh H, et al. Sparse convolutional neural networks[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015: 806−814.
[21] Xu Q, Pan G. SparseConnect: regularising CNNs on fully connected layers[J]. Electronics Letters, 2017, 53(18): 1246−1248. doi:  10.1049/el.2017.2621
[22] Rodriguez-Galiano V F, Ghimire B, Rogan J, et al. An assessment of the effectiveness of a random forest classifier for land-cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 93−104. doi:  10.1016/j.isprsjprs.2011.11.002.
[23] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Advances in neural information processing systems 25. Lake Tahoe: Curran Associates, Inc., 2012: 1106−1114.
[24] 王红霞, 周家奇, 辜承昊, 等. 用于图像分类的卷积神经网络中激活函数的设计[J]. 浙江大学学报(工学版), 2019, 53(7):1363−1373.

Wang H X, Zhou J Q, Gu C H, et al. Design of activation function in CNN for image classification[J]. Journal of Zhejiang University (Engineering Science), 2019, 53(7): 1363−1373.