城市环境中机载传感器的组合

A. Dimmeler, H. Schilling, M. Shimoni, D. Bulatov, W. Middelmann
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引用次数: 6

摘要

在过去的几十年里,城市地区的军事行动变得更加重要。在这些复杂的环境中,详细的态势感知对于成功的作战至关重要。2011年,在比利时泽布吕赫市的一个普通现场试验中,在EDA(欧洲防务局)的“城市场景中使用联合机载成像传感器探测”(DUCAS)项目中,产生了大量高光谱和高空间分辨率数据以及三维(3D)激光数据。在DUCAS框架下,提出了两个层次的处理方法。在第一级,单一传感器数据用于土地覆盖制图和探测感兴趣的目标(即人员、车辆和物体)。在第二阶段,数据融合应用于像素级和信息级,以研究在操作环境中结合传感器系统的好处。为任务规划和制图提供数据是空中侦察的一项重要任务,它包括创建或更新高质量的二维和三维地图。在DUCAS中,使用半自动方法和广泛的传感器数据(高光谱、激光雷达、高分辨率正射影像和视频数据)创建非常详细的土地覆盖地图以及城市地形模型。将不同传感器获取的不同信息结合起来,增加了信息的含量和提取信息的质量。在本文中,我们将介绍用于创建2D/3D地图的先进方法,展示结果和融合多传感器数据的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combined airborne sensors in urban environment
Military operations in urban areas became more relevant in the past decades. Detailed situation awareness in these complex environments is crucial for successful operations. Within the EDA (European Defence Agency) project on “Detection in Urban scenario using Combined Airborne imaging Sensors” (DUCAS) an extensive data set of hyperspectral and high spatial resolution data as well as three dimensional (3D) laser data was generated in a common field trial in the city of Zeebrugge, Belgium, in the year 2011. In the frame of DUCAS, methods were developed at two levels of processing. In the first level, single sensor data were used for land cover mapping and the detection of targets of interest (i.e. personnel, vehicles and objects). In the second level, data fusion was applied at pixel level as well as information level to investigate the benefits of combining sensor systems in an operational context. Providing data for mission planning and mapping is an important task for aerial reconnaissance and it includes the creation or the update of high quality 2D and 3D maps. In DUCAS, semi-automatic methods and a wide range of sensor data (hyperspectral, LIDAR, high resolution orthophotos and video data) were used for the creation of highly detailed land cover maps as well as urban terrain models. Combining the diverse information gained by different sensors increases the information content and the quality of the extracted information. In this paper we will present advanced methods for the creation of 2D/3D maps, show results and the benefit of fusing multi-sensor data.
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