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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >MULTI SCALE OBJECT BASED DETECTION AND CLASSIFICATION OF ROADS AND VEHICLES IN HIGH RESOLUTION OPTICAL SATELLITE IMAGERY
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MULTI SCALE OBJECT BASED DETECTION AND CLASSIFICATION OF ROADS AND VEHICLES IN HIGH RESOLUTION OPTICAL SATELLITE IMAGERY

机译：基于多尺度对象的高分辨率光学卫星图像中的道路和车辆的检测与分类

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In the framework of defence and security applications, NLR has built a demonstration environment and did experiments to use object based image interpretation for the detection of roads and vehicles in single or multiple optical images. The usage of an object based approach provides the ability to include part of the operator knowledge into the software based interpretation process. The method of working for the optical road and target extraction in a single image is developed in a multi scale, multi method scheme in which three levels can be recognized. Firstly the course level, in which in an autonomous way Regions Of Interest (ROI's) are determined. Secondly the medium level, in which basic segmentation and classification is done within the ROI's, with some user interaction. Thirdly in the fine level, the classification result is refined based on the knowledge on object characteristics and contextual rules. On this level, considerable user interaction is required. For detected vehicles a form of type matching can be done depending on the image resolution. For detected road segments, cleaning and network building steps are applied. If multiple images are available, a change detection scheme is added at the medium level. The schemes have been implemented in a COTS software environment. The method has been tested on a high resolution dataset, consisting of multispectral aerial photographs and satellite images. The results show a relevant first achievement, but improvements are required. Options are more accurate correction for the 3D object geometry and more extended use of relational knowledge and probabilities.
机译：在国防和安全应用的框架，NLR已经建立了一个演示环境，并做了实验，使用基于对象的图像判读用于检测单个或多个光学图像道路和车辆的。基于对象方法的使用提供了包括运营商知识到基于软件的解释过程的一部分的能力。用于在单个图像的光路和目标提取工作的方法，在多尺度，其中三个级别可以识别多方法方案开发的。首先课程水平，其中以自主的方式的区域利益（投资回报率）被确定。其次中等水平，其中基本分割和分类中的ROI的完成，有一些用户交互。第三在细级别，分类结果是基于对象的特点和上下文规则的知识细化。在这个层面上，需要大量的用户交互。对于检测到的车辆类型匹配的形式可以根据图像分辨率来完成。对于检测到的道路段，被施加的清洁和网络构建步骤。如果多个图像是可用的，一个变化检测方案是在中等水平加入。该方案已在COTS软件环境中实现。该方法已被测试的高分辨率数据集，由多光谱航空照片和卫星图像。结果显示相关的第一项成就，但需要改进。选项是3D对象的几何形状更精确的校正，更长时间使用关系的知识和概率。

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