Long Image Time Series for Crop Extraction Based on the Automatically Generated Samples Algorithm
文献类型： 会议论文
第一作者： Nan Jia
作者： Nan Jia 1 ; Yinshuai Li 2 ; Andrés Vi?a 1 ; Jie Cheng 3 ; Yue Dou 4 ; Qian Song 5 ; Liuyue He 6 ; Jianguo Liu 1 ;
作者机构： 1.Center for Systems Integration and Sustainability, Michigan State University, East Lansing, USA
2.Department of Landscape Architecture, School of Design, Shanghai Jiao Tong University, Shanghai, China
3.School of Geographic Sciences, East China Normal University, Shanghai, China
4.Department of Natural Resources, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherland
5.Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
6.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
关键词： Training;Machine learning algorithms;Heuristic algorithms;Time series analysis;Crops;Machine learning;Feature extraction
会议名称： IEEE International Geoscience and Remote Sensing Symposium
主办单位：
页码： 3502-3505
摘要： High quality training samples are essential for crop mapping. However, since traditional sample acquisition methods are based on expert interpretation or field research, they are time-consuming and expensive. Using the unique time window of a crop, it is possible to distinguish a specific crop from other features. Therefore, using phenological information combined with machine learning methods for sample migration is a very feasible solution for crop mapping. In this study, we developed a yearly automated generated sample migration algorithm based on crop phenological features. Using image time series derived from data acquired by Landsat sensor systems 5, 7, 8 accessible through the Google Earth Engine cloud data platform, we developed a procedure for temporally displacing ground-truth soybean samples based on phenological features of the crop. With these data, we then generated annual maps of soybean in Heilongjiang Province, China. Overall accuracy of the temporally displaced soybean samples was higher than 95%, while the overall accuracy of the soybean maps obtained was more than 83%. This study provides a feasible approach for developing ground-truth samples from long term image time series, suitable for mapping the dynamics of crops across space and time.
分类号： tp7-53
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