[1] |
Ayyadevara V Kishore. 2018. Random Forest. In: Ayyadevara V Kishore. Pro Machine Learning Algorithms. Hyderabad, Andhra Pradesh, India: Apress, 105-116.
|
[2] |
Benny A H and Dawson G J . 1983. Satellite Imagery as an Aid to Bathymetric Charting in the Red Sea. The Cartographic Journal, 20:5-16.
|
[3] |
曹彬才 . 2017. 遥感测深数据处理方法研究. 郑州:战略支援部队信息工程大学.
|
|
[ Cao Bincai . 2017. A Study of Remotely-Sensed Data Processing in Bathymetry. Zhenzhou: Information Engineering University. ]
|
[4] |
Chen T and Guestrin C . 2016. XGBoost: A Scalable Tree Boosting System. ( 2016-03-09) [2019-06-25]. .
|
[5] |
Cherkassky V and Ma Y . 2004. Practical Selection of SVM Parameters and Noise Estimation for SVM Regression. Neural Networks, 17(1):113-126.
doi: 10.1016/S0893-6080(03)00169-2
pmid: 14690712
|
[6] |
程淑萍, 谭建军, 门婧睿 . 2019. 基于机器学习方法的非编码RNA-蛋白质相互作用的预测. 北京生物医学工程, 38(4):353-359.
|
|
[ Cheng Shuping, Tan Jianjun and Men Jingrui . 2019. Prediction of Non-Coding RNA-Protein Interactions Based on Machine Learning Methods. Beijing Biomedical Engineering, 38(4):353-359. ]
|
[7] |
Cutler A, Cutler D R and Stevens J R . 2004. Random Forests. Machine Learning, 45(1):157-176.
|
[8] |
郭晓雷, 邱振戈, 沈蔚, 栾奎峰, 曹彬才, 吴忠强 . 2017. 基于WorldView-2遥感影像的龙湾港浅海水深反演. 海洋学研究, 35(3):27-33.
|
|
[ Guo Xiaolei, Qiu Zhenge, Shen Wei, Luan Kuifeng, Cao Bincai and Wu Zhongqiang . 2017. Shallow Water Depth Inversion in Longwan Port Based on WorldView-2 Remote Sensing Image. Journal of Marine Sciences, 35(3):27-33. ]
|
[9] |
哈林顿 . 2014. 机器学习实战. 李锐, 李鹏,曲亚东,王斌. 译. 北京:人民邮电出版社.
|
|
[ Harrington. 2014. Machine Learning in Action. Li Rui, Li Peng, Qu Yadong and Wang Bin. Translated. Beijing: People's Posts and Telecommunications Press. ]
|
[10] |
John M P and Robert E S . 1983. Water Depth Mapping from Passive Remote Sensing Data under a Generalized Ratio Assumption. Applied Optics, 22(8):1134-1135.
doi: 10.1364/AO.22.001134
pmid: 20404863
|
[11] |
Lyzenga D R . 1978. Passive Remote Sensing Techniques for Mapping Water Depth and Bottom Features. Applied Optics, 17(3):379.
doi: 10.1364/AO.17.000379
pmid: 20174418
|
[12] |
刘方园, 王水花, 张煜东 . 2018. 支持向量机模型与应用综述. 计算机系统应用, 27(4):1-9.
|
|
[ Liu Fangyuan, Wang Shuihua and Zhang Yudong . 2018. Overview of Support Vector Machine Models and Applications. Computer System Applications, 27(4):1-9. ]
|
[13] |
梅州水库管理处. 2017. 梅州水库水下地形测量报告. 广州:梅州水库管理处.
|
|
[ Meizhou Reservoir Management Office. 2017. Meizhou Reservoir Underwater Topographic Survey Report. Guangzhou: Meizhou Reservoir Management Office. ]
|
[14] |
Shannon C E . 1948. A Mathematical Theory of Communication. The Bell System Technical Journal, 27(3):379-423.
|
[15] |
Stumpf R F, Holde R K and Sinclai R M . 2003. Determination of Water Depth with High-Resolution Satellite Imagery over Variable Bottom Types. Limnology & Oceanography, 48:547-556.
|
[16] |
Thanh P N and Kappas M . 2017. Comparison of Random Forest, K-Nearest Neighbor and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18(1):18.
|
[17] |
王艳姣, 张鹰 . 2005. 基于BP人工神经网络的水体遥感测深方法研究. 海洋工程, 23(4):33-38.
|
|
[ Wang Yanjiao and Zhang Ying . 2005. Study on Remote Sensing of Water Depth Based on BP Artificial Neural Networks. Ocean Engineering, 23(4):33-38. ]
|
[18] |
王艳姣, 董文杰, 张培群 . 2007. 水深可见光遥感方法研究进展. 海洋通报, 26(5):92-101.
|
|
[ Wang Yanjiao, Dong Wenjie and Zhang Peiqun . 2007. Advances in Water Depth Visible Light Sensing Methods. Marine Notification, 26(5):92-101. ]
|
[19] |
王锦锦, 马毅, 张靖宇 . 2018. 基于模糊隶属度的多核SVR遥感水深融合探测. 海洋环境科学, 37(1):130-136.
|
|
[ Wang Jinjin, Ma Yi and Zhang Jingyu . 2018. Multi-Core SVR Remote Sensing Water Depth Fusion Detection Based on Fuzzy Membership. Marine Environmental Science, 37(1):130-136. ]
|
[20] |
许允之, 王舒萍 . 2019. 基于随机森林算法的徐州雾霾回归预测模型. 环境工程, 37(S):170-174,180.
|
|
[ Xu Yunzhi and Wang Shuping . 2019. Xuzhou Haze Regression Prediction Model Based on Stochastic Forest Algorithm. Environmental Engineering, 37(S):170-174, 180. ]
|
[21] |
赵英时 . 2013. 遥感应用分析原理与方法. 北京: 科学出版社.
|
|
[ Zhao Yingshi. 2013. Principles and Methods of Remote Sensing Application Analysis. Beijing: Science Press.]
|