Identification of Winter Wheat in Huang-Huai-Hai Plain Based on Multi-source Optical Radar Data Fusion

被引：0
|
作者：
Feng Q. [1 ,2 ]
Ren Y. [1 ]
Yao X. [1 ,2 ]
Niu B. [1 ]
Chen B. [1 ]
Zhao Y. [1 ,2 ]
机构：
[1] College of Land Science and Technology, China Agricultural University, Beijing
[2] Key Laboratory for Agricultural Land Quality Monitoring and Control, Ministry of Natural Resources, Beijing
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2023年 / 54卷 / 02期
关键词：
deep learning; Google Earth Engine; machine learning; multi-source data fusion; remote sensing imagery classification; winter wheat;
D O I：
10.6041/j.issn.1000-1298.2023.02.015
中图分类号：
学科分类号：
摘要：
Current remote sensing technology can quickly and accurately obtain the spatial distribution information of crops. In order to explore the spatial distribution information of winter wheat in the Huang-Huai-Hai Plain in 2021, based on the Google Earth Engine (GEE) cloud platform. Sentinel-1 SAR radar image and Sentienl-2 optical remote sensing image were used as data sources, the spatial distribution information of winter wheat in the study area was extracted by computing polarization characteristics, spectral characteristics and texture characteristics, using four machine learning methods and deep learning network model. The classification accuracy of each classifier and network architecture was compared. The results showed that the total area of winter wheat in the Huang-Huai-Hai Plain was 16226667hm2, accounting for 49.17% of total area of the study area. The winter wheat planting area was the largest in Henan Province, accounting for 4647334hm2. The winter wheat planting distribution in the study area showed a decreasing trend from east to west and from south to north. Random forest was the classifier with the highest recognition accuracy among the four machine learning methods, with an overall classification accuracy of 94.30%. In the random forest algorithm, the overall accuracy of only using Sentinel-1 radar data was 87.38%, and the overall accuracy of only using Sentinel-2 optical data was 93.95%, while the overall accuracy of the fusion sequence Sentinel active and passive remote sensing data was 94.30%. In a wide range of winter wheat classification, the generalization of deep learning model was higher than that of machine learning. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
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页码：160 / 168
页数：8
相关论文
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