Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890, USA.
IEEE Trans Image Process. 2001;10(4):584-97. doi: 10.1109/83.913593.
Hyperspectral sensors collect hundreds of narrow and contiguously spaced spectral bands of data. Such sensors provide fully registered high resolution spatial and spectral images that are invaluable in discriminating between man-made objects and natural clutter backgrounds. The price paid for this high resolution data is extremely large data sets, several hundred of Mbytes for a single scene, that make storage and transmission difficult, thus requiring fast onboard processing techniques to reduce the data being transmitted. Attempts to apply traditional maximum likelihood detection techniques for in-flight processing of these massive amounts of hyperspectral data suffer from two limitations: first, they neglect the spatial correlation of the clutter by treating it as spatially white noise; second, their computational cost renders them prohibitive without significant data reduction like by grouping the spectral bands into clusters, with a consequent loss of spectral resolution. This paper presents a maximum likelihood detector that successfully confronts both problems: rather than ignoring the spatial and spectral correlations, our detector exploits them to its advantage; and it is computationally expedient, its complexity increasing only linearly with the number of spectral bands available. Our approach is based on a Gauss-Markov random field (GMRF) modeling of the clutter, which has the advantage of providing a direct parameterization of the inverse of the clutter covariance, the quantity of interest in the test statistic. We discuss in detail two alternative GMRF detectors: one based on a binary hypothesis approach, and the other on a "single" hypothesis formulation. We analyze extensively with real hyperspectral imagery data (HYDICE and SEBASS) the performance of the detectors, comparing them to a benchmark detector, the RX-algorithm. Our results show that the GMRF "single" hypothesis detector outperforms significantly in computational cost the RX-algorithm, while delivering noticeable detection performance improvement.
高光谱传感器可以采集数百个狭窄且连续的光谱带数据。这种传感器提供了完全注册的高分辨率空间和光谱图像，对于区分人造物体和自然杂波背景非常有价值。为了获得这种高分辨率数据，需要付出高昂的代价，即产生非常大的数据组，单个场景的数据量就达到几百兆字节，这使得数据的存储和传输变得困难，因此需要快速的板载处理技术来减少传输的数据量。在飞行中处理这些海量高光谱数据时，尝试应用传统的最大似然检测技术会受到两个限制：首先，它们将杂波视为空间白噪声，从而忽略了其空间相关性；其次，其计算成本很高，如果不进行显著的数据减少（例如将光谱带分组为聚类），则会变得非常昂贵，从而导致光谱分辨率的损失。本文提出了一种最大似然检测器，可以成功地解决这两个问题：我们的检测器不仅没有忽略空间和光谱相关性，反而利用它们来发挥优势；而且它的计算效率高，其复杂度仅随可用的光谱带数量线性增加。我们的方法基于杂波的高斯-马尔可夫随机场（GMRF）建模，它具有直接参数化杂波协方差的逆的优势，这是检验统计量中的感兴趣量。我们详细讨论了两种替代的 GMRF 检测器：一种基于二元假设方法，另一种基于“单一”假设方法。我们使用真实的高光谱图像数据（HYDICE 和 SEBASS）对检测器进行了广泛的性能分析，将其与基准检测器 RX 算法进行了比较。我们的结果表明，GMRF“单一”假设检测器在计算成本方面明显优于 RX 算法，同时提供了显著的检测性能改进。
