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Detection and recognition of vehicles in high-resolution SAR imagery

机译:在高分辨率SAR图像中检测和识别车辆

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摘要

Designing SAR sensors is an extremely complex process. Thereby it is very important to keep in mind the goal for which the SAR sensor has to be built. For military purpose the detection and recognition of vehicles is essential. To give recommendations for design and use of SAR sensors we carried out interpreter experiments. To assess the interpreter performance we measured performance parameters like detection rate, false alarm rate etc. The following topics were of interest: How do the SAR sensor parameters bandwidth and incidence angle influence the interpreter performance? Could the length, width and orientation of vehicles be measured in SAR-images? Which information (size, signature ...) will be used by the interpreters for vehicle recognition? Using our SaLVe evaluation testbed we prepared lots of images from the experimental SAR-system DOSAR (EADS Dornier) and defined several military interpretation tasks for the trials. In a 4 weeks experiment 30 German military photo interpreters had to detect and classify tanks and trucks in X-Band images with different resolutions. To accustom the interpreters to SAR image interpretation they carried out a computer based SAR tutorial. To complete the investigations also subjective assessment of image quality was done by the interpreters.
机译:设计SAR传感器是一个极其复杂的过程。因此,牢记必须构建SAR传感器的目标非常重要。为了军事目的,车辆的检测和识别是必不可少的。为了提供有关SAR传感器设计和使用的建议,我们进行了解释器实验。为了评估口译员的表现,我们测量了诸如检出率,误报率等性能参数。以下主题值得关注:SAR传感器参数的带宽和入射角如何影响口译员的表现?可以在SAR图像中测量车辆的长度,宽度和方向吗?口译员将使用哪些信息(大小,签名...)来识别车辆?使用我们的SaLVe评估测试台,我们从实验性SAR系统DOSAR(EADS Dornier)准备了许多图像,并为试验定义了一些军事解释任务。在一个为期4周的实验中,有30名德国军事照片翻译人员必须以不同分辨率在X波段图像中对坦克和卡车进行检测和分类。为了使口译员习惯于SAR图像解释,他们进行了基于计算机的SAR教程。为了完成调查,口译人员还对图像质量进行了主观评估。

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