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Automatic target recognition for hyperspectral imagery using high-order statistics

机译：使用高阶统计量自动识别高光谱图像的目标

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摘要

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective
机译：由于高光谱成像传感器的最新发展，现在可以发现许多微弱的未知信号源，这些信号源无法通过多光谱传感器解决，从而可以进行目标检测，识别和识别。由于通常无法获得有关此类源的信息，因此自动目标识别（ATR）对高光谱图像分析人员提出了巨大挑战。过去几年中为ATR开发的许多方法都基于二阶统计。本文研究使用高阶统计量的ATR技术。对于高光谱图像中的ATR，大多数有趣的目标通常以低概率和小人口出现，并且通常无法通过二阶统计量来描述。在这种情况下，本文的实验表明，使用高阶统计量执行目标检测比使用二阶统计量更有效。为了在确定需要检测的信号源的数量时进一步解决一个具有挑战性的问题，最近开发的虚拟维数（VD）概念被用于估计该数量。实验表明，结合VD使用基于高阶统计的技术来执行ATR确实非常有效

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