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摘要

全文摘要次数: 3400 全文下载次数: 99
引用本文:

DOI:

10.11834/jrs.20100206

收稿日期:

2008-05-19

修改日期:

2009-06-01

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地性线的山地区域的卫星影像几何精纠正
西南林学院 云南省森林灾害预警与控制重点实验室, 云南 昆明 650224
摘要:

提出了一种山地区域基于DEM地性线的控制纠正新方法, 该方法以数字地形模型DEM为无几何变形的控制基准纠正卫星影像。阐述了提取沟谷、山脊、山峰和凹地区域的地性线的原理和算法, 给出了山地区域基于地性线进行卫星图像几何精纠正实施步骤, 进一步讨论了地性线提取、控制点采集存在的问题, 以及解决问题的途径。实验结果表明, 对于山地区域, 地性线的空间数量数倍于水系、道路等常规地图层; 地性线来源于DEM, 其空间稳定性和可靠性更高, 可以用于山地区域的卫星影像的严格控制纠正。用该方法进行几何纠正处理, 几何误差

Highly accurate geometric correction of satellite images of moun-tain areas
Abstract:

Highly accurate geometric correction is fundamental to the application of satellite images. In highly accurate geometric correction of remote sensing images for precise match to the base maps, the difficulties in capturing ground control points (GCPs), insufficient number of GCPs and low accuracy of the geo-referenced images remain to be frequent issues. In search for solutions to these issues, this paper proposes a new method for the geometric correction of satellite images for the mountain ar-eas based on the terrain feature lines extracted from the Digital Elevation Model (DEM). This method uses DEM as the base maps without any geometric distortion for the geometric correction of satellite images. This paper presents the theories and method to extract terrain feature lines, such as gorges, mountain ridges, peaks and concaves and describes the procedures for the geometric correction for satellite images of mountain areas based on the terrain feature lines. It further discusses the problems in extracting terrain feature lines and capturing of GCPs, as well as the solutions to these issues. The outcome from the experiments shows the number of terrain feature lines of mountains areas are a great many times of those in the conventional map layers of hydrological systems and road networks, and that terrain feature lines extracted from DEM present high stability and reliability and can be applied in the highly accurate geometric correction for satellite images of mountain areas. The results of statistical test show the average geometric error is reduced to the size of one pixel when this method is used in geometric correction.

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