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Extensions of the U.S. Night Vision Laboratory static performance model for thermal viewing systems on structural targets and backgrounds in cluttered scenes

机译:美国夜视实验室静态性能模型的扩展,用于在杂乱场景中的结构目标和背景上的热观察系统

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Abstract: The search procedure and target acquisition in cluttered scenes are highly dependent on the contrast between target and background, and the scene content. The latter varies widely from uniform displays of sky or sea to highly complex displays of mixed landscapes or urban areas. Generally, contrast should be defined within the limit of the geometrical resolution of the sensor system. Both target and background will be structured in respect of optical radiance or thermal emittance. Lillesaeter has presented a contrast definition allowing for structured as well as plain targets and backgrounds. It consists of two parts, area contrast and edge contrast, both of which are logarithmic functions of target/background radiance ratios. This contrast definition has been implemented in the Night Vision Laboratory (NVL) model to simulate acquisition of thermally structured targets in complex backgrounds. As far as the effect of clutter makes the task of discriminating the target from background more difficult, videosimulations have been used to correlate the NVL model to different levels of scene complexity. This paper presents the NDRE modification of the NVL model and gives some examples related to the efficiency of thermal camouflage. !5
机译:摘要:杂乱场景中的搜索过程和目标获取高度依赖于目标与背景之间的对比度以及场景内容。后者的变化范围很广,从天空或海洋的均匀显示到混合景观或城市区域的高度复杂的显示。通常,对比度应在传感器系统的几何分辨率范围内定义。目标和背景都将在光辐射或热发射方面进行构造。利勒萨特(Lillesaeter)提出了一种对比度定义,可以使用结构化目标以及纯色目标和背景。它由区域对比度和边缘对比度两部分组成,这两个部分都是目标/背景辐射比的对数函数。夜视实验室(NVL)模型中已实现了这种对比度定义,以模拟复杂背景下热结构目标的采集。就杂波的影响使将目标与背景区分开的任务更加困难,视频模拟已用于将NVL模型与不同级别的场景复杂性相关联。本文介绍了NVL模型的NDRE修改,并提供了一些与热伪装效率有关的示例。 !5

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