针对识别和定位路面上每辆汽车这一交通遥感图像处理的核心环节,提出一种解决方法.应用光照模型推导出路面、车辆(深浅两类)、汽车阴影在全色遥感影像中的亮度差异与亮度变化特征.以亮度差异为基础建立了能够将车辆区域从路面遥感图像中分割出来的图层分离算法.针对密集行驶的汽车因阴影相互覆盖而容易被误识别为一辆大型车的问题以及浅色车因深色车窗造成的识别结果割裂问题,利用亮度变化特征以及阴影、车窗与汽车的位置关系设计了车辆区域内的阴影和车窗干扰消除算法,通过闭运算实现了遥感图像中的汽车识别与定位.选用10幅交通遥感图像进行了测试,对浅色车的识别率大于92%,对深色车的识别率大于87%.
For the issue of vehicles recognition and location, which is the key of traffic RS information processing, an image processing method is presented. Based on the Phong model, features of luminance difference and luminance variance of common objects, including vehicles (dark and bright) and their shadows and road surface, are extracted. By taking advantage of the features of luminance difference, the vehicle regions, containing vehicle and its shadow, are extracted through layer separation firstly. And then, in order to suppress interference caused by vehicle shadow which is easy to cause the connection between abreast running vehicles, as well as suppress interference caused by vehicle window which might disturb the bright vehicle identification, an algorithm is designed according to the features of luminance variance and the position relationship between vehicle and its shadow and window. Through morphological method, the vehicles of different colors are extracted and located on the RS-image. This method had been tested, and more than 92% bright vehicles and 87% dark vehicles are detected.
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