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应用高分辨率卫星影像提取水土保持措施信息的分类后处理技术研究
引用本文:王冬梅,吴卿,王西林,刘程里.应用高分辨率卫星影像提取水土保持措施信息的分类后处理技术研究[J].中国水土保持,2006(5):42-43.
作者姓名:王冬梅  吴卿  王西林  刘程里
作者单位:1. 北京林业大学,水土保持学院,教育部水土保持与荒漠化防治重点实验室,北京,100083
2. 北京林业大学,水土保持学院,教育部水土保持与荒漠化防治重点实验室,北京,100083;黄河水利科学研究院,河南,郑州450003
3. 邵阳大圳管理局,东风水电管理站,湖南,邵阳,422400
摘    要:在应用高分辨率卫星影像进行水土保持措施信息的提取过程中,遥感分类图像后处理方法对不同类别的水土保持措施信息的提取精度具有重要影响。采用空间分辨率为2.5 m的卫星影像数据,针对黄土丘陵区水土保持措施的信息提取,用不同大小的聚类处理参数对各类型的措施面积精度进行了检验,在综合考虑水土保持措施的空间特征与图像特征基础上,确立了采用Spot5高分辨率卫星影像进行水土保持措施信息的遥感分类时,宜选用最小图斑为6×6个像元的参数值。

关 键 词:水土保持措施  高分辨率卫星影像  遥感分类后处理技术  聚类处理  最小图斑
文章编号:1000-0941(2006)05-0042-02
收稿时间:2006-02-28
修稿时间:2006年2月28日

Study on Post Processing Technology of Extracting Data of Soil and Water Conservation Measures by Applying High Resolving Power Satellite Images
WANG Dong-mei,WU Qing.Study on Post Processing Technology of Extracting Data of Soil and Water Conservation Measures by Applying High Resolving Power Satellite Images[J].Soil and Water Conservation In China,2006(5):42-43.
Authors:WANG Dong-mei  WU Qing
Institution:WANG Dong-mei~1,WU Qing~
Abstract:The post processing method of remote sensing classified images is of important influence to extracting precision of the data of different types of soil and water conservation measures during the process of data extraction by applying high resoling power satellite images.It verifies the precision of area with various measures by using 2.5 m satellite image data of spatial resolution,in the light of the data extraction of soil and water conservation measures in the gullied rolling loess area and using different parameters of clustering process.It is better to select parameter value of 6×6 pixels of the minimum spot while using Spot 5 high resolving power satellite image for remote sensing classification of soil and water conservation measures based on integrated consideration of spatial and image characteristics of soil and water conservation measures.
Keywords:soil and water conservation measures  high resoling power satellite image  post-processing technology of remote sensing classification  clustering process  minimum spot  
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