Automatic Interpretation of Spatial Distribution of Winter Wheat Based on Random Forest Algorithm to Optimize Multi-temporal Features

被引：0
作者：
Li X. [1 ,2 ]
Liu S. [1 ,2 ]
Li L. [1 ,2 ]
Jin Y. [1 ,2 ]
Fan W. [1 ,2 ]
Wu L. [3 ]
机构：
[1] Institute of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang
[2] Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang
[3] School of Infomation Engineering, China University of Geosciences (Beijing), Beijing
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2019年 / 50卷 / 06期
关键词：
Automatic interpretation; Multi-temporal; Random forest algorithm; Winter wheat;
D O I：
10.6041/j.issn.1000-1298.2019.06.024
中图分类号：
学科分类号：
摘要：
In order to explore how to use the remote sensing image automatic interpretation technology to realize the winter wheat planting statistics survey and improve its extraction accuracy,the Gaofen-2 remote sensing image data of six key growth periods of winter wheat were selected. One of the most sensitive features to winter wheat area was selected respectively as the input variable from six features of near-infrared gray (NIR), red band gray (R), green band gray (G), blue wave band gray (B), ratio vegetation index (RVI) and normalized difference vegetation index (NDVI). One feature was selected for each time phase, and six features were selected for the six time phases. A model was constructed by using the random forest algorithm to extract winter wheat. The training set was constructed by selecting land samples with different growth and planting varieties in the study area. The model was constructed based on the multi-temporal features and applied to the whole Dachang Hui Autonomous County. The spatial distribution of winter wheat in Dachang Hui Autonomous County was obtained. Compared with the statistical results, the recognition accuracy of the model constructed by multi-temporal feature optimization was close to 90%. After sample optimization and post-processing, the accuracy can still be improved. This method can quickly extract winter wheat on the premise of ensuring the extraction accuracy, and greatly improve the corresponding work efficiency. © 2019, Chinese Society of Agricultural Machinery. All right reserved.
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页码：218 / 225
页数：7
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