干旱区生态环境知识资源中心(Arid): The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine

Arid

DOI	10.3390/rs14122758
	The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine
	Yao, Jinxi; Wu, Ji; Xiao, Chengzhi; Zhang, Zhi; Li, Jianzhong
通讯作者	Zhang, Z
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:12
英文摘要	The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning.
英文关键词	crops classification Sentinel-1 Sentinel-2 machine learning deep learning Google Earth Engine Google Colab
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000816420200001
WOS关键词	PADDY RICE AGRICULTURE ; LAND-COVER
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394159
推荐引用方式 GB/T 7714	Yao, Jinxi,Wu, Ji,Xiao, Chengzhi,et al. The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine[J],2022,14(12).
APA	Yao, Jinxi,Wu, Ji,Xiao, Chengzhi,Zhang, Zhi,&Li, Jianzhong.(2022).The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine.REMOTE SENSING,14(12).
MLA	Yao, Jinxi,et al."The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine".REMOTE SENSING 14.12(2022).