高分辨率卫星图像中车辆检测的研究

L. Xie, Liying Wei
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引用次数: 10

摘要

随着卫星分辨率的提高和卫星图像中面向对象的检测方法,与传统的数据获取方法相比,在大面积卫星图像中可以更快、更广泛地获取交通数据。采用图像增强技术,首先提高图像质量,然后利用多尺度分割技术和监督分类方法从卫星图像中检测车辆。在此过程中,总结出三种不同情况下的车辆分类决策树。最后,利用Worldview-2和GeoEye-1对城市道路典型区域的遥感影像进行了实证研究。通过对实验结果的精度分析,表明该方法的平均精度在90%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Vehicle Detection in High Resolution Satellite Images
With the improvement of satellite resolution and the object-oriented detection method in satellite images, traffic data can be more quickly and widely acquired in large area satellite images compared with the traditional data acquired method. With the technology of image enhancement, the paper improved the image quality first, and then utilized the multi-scale segmentation technology and supervised classification method to detect the vehicle from satellite images. In the process, three classification decision trees for vehicles in different situations have been summed up. At last, the paper has achieved the empirical research using the remote sensing images of typical regions in the urban road from Worldview-2 and the GeoEye-1. Based on the precision analysis of the experimental results, it shows that the average accuracy is more than 90%.
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