干旱区生态环境知识资源中心(Arid): 机器学习法的干旱区典型农作物分类

Arid

	机器学习法的干旱区典型农作物分类
其他题名	Study of Typical Arid Crops Classification Based on Machine Learning
	黄双燕 1; 杨辽 2; 陈曦2 ; 姚远 1
来源期刊	光谱学与光谱分析
ISSN	1000-0593
出版年	2018
卷号	38 期号:10 页码:3169-3176
中文摘要	当前,基于机器学习方法开展农作物分类研究,对于确保干旱区粮食安全和生态安全有着极为重要的现实意义。基于机器学习方法,采用时间序列Sentinel 2A遥感数据提取农作物分类信息,通过引入地块基元和红边特征,探讨了不同分类特征组合对机器学习分类精度的影响。结果表明:随机森林分类器可以有效集成光谱和植被指数等多维向量的优势,将其应用于干旱区典型农作物分类上的精度均在89%以上,分类组总体精度最高可达94.02%。地块基元点集支持下的分类特征提取方法能够提高机器学习效率和农作物分类精度,使光谱组及指数组的分类精度分别提高3.13%和4.07%,并能有效解决椒盐效应及耕地边缘廓线模糊等问题。红边光谱和红边指数的引入分别使随机森林分类器总体精度提高2.39%和1.63%,并使春、冬小麦的识别能力显著提高,表明红边特征能够帮助分类器更敏感地捕捉不同作物特有的生长特性及物候差异。该研究结果可为机器学习方法及Sentinel 2A卫星在干旱区农业遥感的应用提供参考。
英文摘要	Accurate and timely crops classification information is of great significance for arid food security monitoring and ecological management.Adding sensitive waveband and improving classification methods are the major development trends of crops classification.In this paper,we carry out crop classification study based on Sentinel 2Atime-series remote sensing data,and establish an object-oriented parcel point set in study area,trying to explore the influence of using different classification features on machine learning classification accuracy.Results indicate as follows:(1)Random forest classifier can effectively integrate the benefits of multidimensional vectors such as spectral or vegetation index,all the accuracy of different groups in this study are above 89%,while the supreme overall accuracy up to 94.02%.(2)The classification features extraction method,which was supported by object-oriented parcel point set,can resolve the issue of salt-and-pepper noise and fuzzy parcel boundary well.Meanwhile, it also improves the efficiency and accuracy of machine learning classifier,which can be demonstrated by the result that the classification accuracy of spectral group and index group increased by 3.13%and 4.07%respectively.(3)Red-edge features can help the classifier to capture the phenological differences and unique growth characteristics of different crops.And the introduction of the red-edge spectrum and red-edge index can improve the classification accuracy by 2.39%and 1.63%respectively, while the recognition ability of spring and winter wheat also improved significantly.The result of this study can be referred for the application of the machine learning method and the Sentinel 2Aremote sensing data in arid agriculture remote sensing.
中文关键词	机器学习 ; 随机森林 ; 农作物分类 ; 地块基元 ; 红边波段
英文关键词	Machine learning Random forest Crop classification Parcel data set Red-edge
语种	中文
国家	中国
收录类别	CSCD
WOS类目	REMOTE SENSING
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:6399087
来源机构	中国科学院新疆生态与地理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/237796
作者单位	1.中国科学院新疆生态与地理研究所;;中国科学院大学, 荒漠与绿洲生态国家重点实验室;;, 乌鲁木齐;;, 新疆;;北京 830011;;100049, 中国; 2.中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐, 新疆 830011, 中国
推荐引用方式 GB/T 7714	黄双燕,杨辽,陈曦,等. 机器学习法的干旱区典型农作物分类[J]. 中国科学院新疆生态与地理研究所,2018,38(10):3169-3176.
APA	黄双燕,杨辽,陈曦,&姚远.(2018).机器学习法的干旱区典型农作物分类.光谱学与光谱分析,38(10),3169-3176.
MLA	黄双燕,et al."机器学习法的干旱区典型农作物分类".光谱学与光谱分析 38.10(2018):3169-3176.