干旱区生态环境知识资源中心(Arid): 高分辨率遥感影像农田林网自动识别

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	高分辨率遥感影像农田林网自动识别
其他题名	Automatic Recognition of Farmland Shelterbelts in High Spatial Resolution Remote Sensing Data
	吕雅慧 1; 张超 2; 郧文聚 3; 李鹏山 4; 桑玲玲 3; 陈英义 1
来源期刊	农业机械学报
ISSN	1000-1298
出版年	2018
卷号	49 期号:1 页码:157-163
中文摘要	以0.5m的GeoEye1卫星遥感影像为数据源,充分利用影像的纹理和光谱信息,研究农田林网高分辨率遥感自动识别方法。首先利用归一化差值植被指数(NDVI)和二维熵值构建分类决策树,并结合辅助数据初步提取出带状的农田林网;其次对该结果进行形态学处理,得到连续、细化的农田林网识别结果。选取甘肃省临泽县河西走廊中部巴丹吉林沙漠南缘绿洲的部分区域为研究区,进行实例验证,结果表明,采用本文构建的方法,农田林网的自动识别精度均在92%以上,平均精度达到92.97%,空间位置吻合度均在86%以上,平均吻合度达到93.13%,满足土地整治等工程监管的实际需求。该方法可为农田林网建设及相关工程监管提供科学支撑。
英文摘要	Farmland shelterbelt is a major component of land reclamation, farmland protection and the engineering construction of ecological environmental protection. And the information acquisition of farmland shelterbelt is an important approach to supervising engineering, such as land reclamation and so on. The automatic recognition method of farmland shelterbelts was explored by taking full advantages of the spectral and morphological features in high spatial resolution remote sensing data based on GeoEye1 satellite remote sensing image (0.5m- resolution). Firstly, the classification decision tree was established by making use of the normalized difference vegetation index (NDVI) and two-dimensional entropy of GeoEye1 satellite remote sensing image. Secondly,the preliminary extraction results of farmland shelterbelts were found out by combining with the auxiliary data. Thirdly, the preliminary extraction results were processed by morphology operations, including expansion, hole filling, de-noising and thinning, to get the continuous and refined extraction results. The partial region of the oasis in the southern margin of Badain Jaran Desert in Linze County, Gansu Province was taken as a typical study area for instance validation, which is located in the central Hexi Corridor. The experimental result indicated that by using the method the automatic recognition precision of farmland shelterbelts were all above 92% and the average accuracy reached 92.97%, the spatial coincidences were all above 86% and the average anastomosis reached 93.13%. So it can satisfy the actual demand in supervising engineering, such as land reclamation and so on. The method can provide scientific support for the farmland shelterbelts construction and the related engineering supervision.
中文关键词	农田林网 ; 遥感 ; 高空间分辨率 ; 决策树 ; 形态学
英文关键词	farmland shelterbelts remote sensing high spatial resolution decision tree morphology
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURAL ECONOMICS POLICY
WOS研究方向	Agriculture
CSCD记录号	CSCD:6151871
来源机构	中国农业大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/238005
作者单位	1.中国农业大学信息与电气工程学院, 北京 100083, 中国; 2.中国农业大学信息与电气工程学院;;国土资源部农用地质量与监控重点实验室, ;;国土资源部农用地质量与监控重点实验室, ;;, 北京;;北京 100083;;100035, 中国; 3.国土资源部农用地质量与监控重点实验室;;国土资源部土地整治中心, 国土资源部农用地质量与监控重点实验室;;, ;;, 北京;;北京 100035;;100035, 中国; 4.成都市国土规划地籍事务中心, 成都, 四川 610074, 中国
推荐引用方式 GB/T 7714	吕雅慧,张超,郧文聚,等. 高分辨率遥感影像农田林网自动识别[J]. 中国农业大学,2018,49(1):157-163.
APA	吕雅慧,张超,郧文聚,李鹏山,桑玲玲,&陈英义.(2018).高分辨率遥感影像农田林网自动识别.农业机械学报,49(1),157-163.
MLA	吕雅慧,et al."高分辨率遥感影像农田林网自动识别".农业机械学报 49.1(2018):157-163.