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Detection and recognition of vehicles in high-resolution SAR imagery

机译:高分辨率SAR图像中车辆的检测与识别

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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传感器是一个非常复杂的过程。因此,请记住必须构建SAR传感器的目标是非常重要的。对于军用目的,车辆的检测和识别至关重要。为设计和使用SAR传感器提供推荐,我们进行了翻译实验。为了评估解释器性能,我们测量了检测率,误报率等的性能参数。以下主题感兴趣:SAR传感器参数带宽和入射角如何影响解释器性能?可以在SAR图像中测量车辆的长度,宽度和方向吗?口译员将用于车辆识别的哪些信息(大小,签名......)?使用我们的Salve评估测试平台我们从实验性SAR系统软质量(EADS DORNIER)中准备了大量图像,并为试验确定了几个军事解释任务。在4周的实验中,德国军事照片口译员必须在具有不同分辨率的X波段图像中检测和分类坦克和卡车。将解释器习惯于SAR图像解释,他们执行了基于计算机的SAR教程。要完成调查,还通过口译员完成对图像质量的主观评估。

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