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Target Detection in Inhomogeneous Non-Gaussian Hyperspectral Data, Based On Non-Parametric Density Estimation

机译:基于非参数密度估计的非均匀非高斯高光谱数据中的目标检测

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Performance of algorithms for target signal detection in Hyperspectral Imagery (HSI) is often deteriorated when the data is neither statistically homogeneous nor Gaussian or when its Joint Probability Density (JPD) does not match any presumed particular parametric model, In this paper we propose a novel detection algorithm which first attempts at dividing data domain into mostly Gaussian and mostly Non-Gaussian (NG) subspaces, and then estimates the JPD of the NG subspace with a non-parametric Graph-based estimator. It then combines commonly used detection algorithms operating on the mostly-Gaussian sub-space and an LRT calculated directly with the estimated JPD of the NG sub-space, to detect anomalies and known additive-type target signals. The algorithm performance is compared to commonly used algorithms and is found to be superior in some important cases.
机译:当数据既不是统计均质也不是高斯的,或者其联合概率密度(JPD)与任何假定的特定参数模型都不匹配时,高光谱图像(HSI)中目标信号检测算法的性能通常会降低。本文提出了一种新颖的方法检测算法,首先尝试将数据域划分为大部分高斯和大部分非高斯(NG)子空间,然后使用基于非参数图的估计器估算NG子空间的JPD。然后,它将在高斯子空间上运行的常用检测算法与直接用NG子空间的估计JPD计算出的LRT相结合,以检测异常和已知的加性目标信号。将算法性能与常用算法进行了比较,发现在某些重要情况下该算法具有优越的性能。

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