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Hyperspectral target detection using multiple platform cuing

机译:使用多平台Cuing的高光谱靶检测

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Hyperspectral imaging has been demonstrated to achieve unresolved object detection through use of the spectral information. However, in many cases, these demonstrations have been in near ideal situations where the use of laboratory spectra with pristine data has lead to success. Complexities introduced in real-world situations such as a cluttered urban environment make successful detection challenging. One approach to improving performance is to use the synergistic effects of multiple sensors surveying a common area. These multiple sensors can be used to cue each other and enhance detection or tracking of objects. For maximum robustness, however one would want to minimize the complexity of processing algorithms such as those used to compensate for atmospheric and illumination effects. This paper investigates the limits of the use of spectra observed under one set of conditions to be used to detect an object under a different set of conditions. The results indicate good performance can be achieved across a reasonable range of illumination and viewing conditions.
机译:已经证明过高光谱成像以通过使用光谱信息来实现未解决的物体检测。然而,在许多情况下,这些示威活动已经在接近理想情况下,使用具有原始数据的实验室光谱导致成功。在杂乱的城市环境中介绍的复杂性在杂乱的城市环境中取得了成功的挑战。提高性能的一种方法是使用调查公共区域的多个传感器的协同效应。这些多个传感器可用于彼此提示并增强对象的检测或跟踪。对于最大稳健性,但是人们希望最小化处理算法的复杂性,例如用于补偿大气和照明效果的处理算法。本文研究了在一组条件下观察到的光谱的限制,以便在不同的条件下检测物体。结果表明,在合理的照明和观察条件范围内可以实现良好的性能。

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