.png)
创新技术
卫星遥感与人工智能进行水质监测
基于高光谱遥感数据和智能算法的安大略湖水质参数反演
Retrieval of Water Quality Parameters in Lake Ontario Based on Hyperspectral Remote Sensing Data and Intelligent Algorithms
EGU2020-1869
https://doi.org/10.5194/egusphere-egu2020-1869
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
Yu Li1,2, Youyue Sun1, Jinhui Jeanne Huang1, and Edward McBean2
1 Nankai University, Sino-Canada Joint R&D Centre for Water and Environmental Safety, College of Environmental Science and Engineering, Tianjin, China (liyuhydro@qq.com)
2 School of Engineering, University of Guelph, N1G 2W1, Canada
基于机器学习的使用 Sentinel-2 图像评价非光学活性水质参数的策略
A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery
International Journal of Remote Sensing
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tres20
Hongwei Guo , Jinhui Jeanne Huang , Bowen Chen , Xiaolong Guo & Vijay P.
Singh (2021) A machine learning-based strategy for estimating non-optically active water quality
parameters using Sentinel-2 imagery, International Journal of Remote Sensing, 42:5, 1841-1866,
DOI: 10.1080/01431161.2020.1846222
利用长期 Landsat 档案深度学习绘制 Simcoe 水质图的性能表现
Performance of deep learning in mapping water quality of Lake Simcoe with long-term Landsat archive