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Information extraction from very high resolution satellite imagery over Lukole refugee camp, Tanzania

机译:坦桑尼亚卢克勒难民营超高分辨率卫星影像的信息提取

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This paper addresses information extraction from IKONOS imagery over the Lukole refugee camp in Tanzania. More specific, it describes automatic image analysis procedures for a rapid and reliable identification of refugee tents as well as their spatial extent. From the identified tents, the number of refugees can be derived and a map of the camp can be generated, which can be used for improving refugee camp management. Four information extraction methods have been tested and compared: supervised classification, unsupervised classification, multi-resolution segmentation and mathematical morphology analysis. The latter two procedures based on object-oriented classifiers perform best with a spatial accuracy above 85% and a statistical accuracy above 91%. These methods could be used for refugee camp information extraction in other geographical settings and on imagery with different spatial and spectral resolutions.
机译:本文介绍了从坦桑尼亚卢克勒难民营的IKONOS影像中提取的信息。更具体地说,它描述了用于快速可靠地识别难民帐篷及其空间范围的自动图像分析程序。从确定的帐篷中,可以得出难民人数,并可以生成营地地图,这些地图可以用于改善难民营的管理。测试并比较了四种信息提取方法:监督分类,非监督分类,多分辨率分割和数学形态分析。基于面向对象分类器的后两个过程在空间精度高于85%且统计精度高于91%时表现最佳。这些方法可用于在其他地理环境中以及在具有不同空间和光谱分辨率的图像上提取难民营信息。

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