李山山等:Optimal selection of GCPs from Global Land Survey 2005 for precision geometric correction of Landsat-8 imagery
被阅读 963 次
2015-10-23
Optimal selection of GCPs from Global Land Survey 2005 for precision geometric correction of Landsat-8 imagery
作者:Li, SS (Li, Shanshan)[ 1 ] ; Peng, M (Peng, Man)[ 2 ] ; Wu, CS (Wu, Changshan)[ 3 ] ; Feng, XX (Feng, Xuxiang)[ 1 ] ; Wu, YW (Wu, Yeiwei)[ 1 ]
EUROPEAN JOURNAL OF REMOTE SENSING
卷: 48  页: 303-318
DOI: 10.5721/EuJRS20154817
出版年: 2015
 
摘要
To conduct precision geometric correction of Landsat-8 data, all ground control points from the Global Land Survey (GLS) 2005 are typically selected, thereby making the process time consuming and labor intensive. This paper developed an optimal selection algorithm for choosing representative points. The optimal technique consists of three steps, including 1) evaluating the spatial distribution patterns of points from GLS2005, 2) extracting ideal points positions from each scene based on the spatial distribution patterns obtained in the first step, and 3) selecting real representatives GCPs from the original large number of GCPs based on the positions of ideal points. One hundred individual Landsat-8 images were chosen for precision geometric correction to assess the robustness and efficiency of the method. Experimental result demonstrated that the approach could only consume 1/10 processing time or less when compared to that using the full set of original GCPs while still achieving comparable geometric accuracy. The developed technique will make an important contribution to improving the efficiency of precision geometric product generation systems for Landsat-8 images.
 
通讯作者地址: Peng, M (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Datun Rd, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, China Remote Sensing Satellite Ground Stn, Beijing 100094, Peoples R China
[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 3 ] Univ Wisconsin, Dept Geog, Milwaukee, WI 53201 USA