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Detection of Urban Features From High Resolution Satellite Images

机译：从高分辨率卫星图像检测城市特征

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One common approach to detect urban features from high resolution images is, using automatic classification methods. The purpose of this paper is to demonstrate the applicability of the developed algorithm (DA) by systematically evaluating its performances in comparison to other popular classifier, support vector machine (SVM). The detection performance of algorithms is evaluated by an object-based criterion. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. The method is applied to two different Quickbird images for complex urban patterns. In evaluation of object based accuracy assessment it is shown that, while, SVM provide higher rates of correct detection it provides higher rates of false alarms. DA, on the other hand, providing tolerable rates of correct detection and lower rates of false alarm.
机译：使用自动分类方法检测来自高分辨率图像的城市特征的一种常见方法。本文的目的是通过系统地评估其与其他流行分类器，支持向量机（SVM）进行系统地评估其性能来证明发达算法（DA）的适用性。通过基于对象的标准来评估算法的检测性能。考虑到一致性，通过标记GIS环境中的建筑边界产生的相同基础事实数据用于准确性评估。该方法应用于复杂城市模式的两个不同的Quickbird图像。在评估基于对象的精度评估时，显示了，虽然SVM提供了更高的正确检测率，但它提供了更高的误报率。另一方面，DA提供可容忍的正确检测和较低误报率的速率。

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