| 城市街区星载光学遥感图像车辆目标自动检测方法 |
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| 引用本文： | 李昭慧,张建奇.城市街区星载光学遥感图像车辆目标自动检测方法[J].红外与激光工程,2014,43(11):3751-3755. |
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| 作者姓名： | 李昭慧 张建奇 |
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| 作者单位： | 1.西安电子科技大学 物理与光电工程学院,陕西 西安 710071 |
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| 摘 要： | 针对星载光学遥感图像城市街区复杂背景问题,提出一种车辆目标自动检测方法.首先,利用场景中植被背景的物理属性,通过多光谱波段抑制场景中的植被背景,然后,在分析城市街区地物形态反射率特性的基础上,利用全色波段并结合二值形态学方法抑制场景中的建筑物,最后,引入著名的RX算法对抑制后的图像进行车辆目标检测.将文中提出的方法应用于实际Quickbird影像的车辆目标检测,结果表明所提出的方法具有鲁棒性强,执行效率高,不需要人工辅助等方面的特点,可用于城市街区车辆目标的自动检测.
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| 关 键 词： | 车辆检测 光学遥感图像 城市街区 形态学 |
| 收稿时间： | 2014-03-14 |
Automatic vehicle detection using spaceborne optical remote sensing images in city area |
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| Li Zhaohui,Zhang Jianqi.Automatic vehicle detection using spaceborne optical remote sensing images in city area[J].Infrared and Laser Engineering,2014,43(11):3751-3755. |
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| Authors: | Li Zhaohui Zhang Jianqi |
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| Affiliation: | 1.School of Physics and Optoelectronic Engineering,Xidian University,Xi'an 710071,China |
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| Abstract: | It is difficult to detect vehicles in city area by using paceborne optical remote sensing images, because the background in city area is too complex. In this paper, an automatic vehicle detection method was proposed to address the issue by using background segmentation method. Firstly, the physical property of the vegetation was analyzed and used to suppress the vegetation background of a scene by using the multi- spectral information of the scene. Next, the reflectance characteristics of city area cover types were analyzed. Based on the reflectance characteristics of building roofs and roads, the building background in the scene was removed by employing the binary morphological method on the panchromatic band image. Finally, the famous RX algorithm was introduced to detect the vehicles on the vegetation and building background suppressed image. The proposed method is applied to the actual Quickbird image for vehicle target detection. The results show that the proposed method has strong robustness, high efficiency, and automatic characteristics, and can be used for vehicle detection in city area. |
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| Keywords: | vehicle detection optical remote sensing images city area morphological |
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