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Robust Detection and Estimation of Change-Points in a Time Series of Multivariate Images

机译：多元图像时间序列中变化点的鲁棒检测和估计

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In this paper, we study the problem of detecting and estimating change-points in a time series of multivariate images. We extend existent works to take into account the heterogeneity of the dataset on a spatial neighborhood. The classic complex Gaussian assumption of the data is replaced by a complex elliptically symmetric assumption. Then robust statistics are derived using Generalized Likelihood Ratio Test (GLRT). These statistics are coupled to an estimation strategy for one or several changes. Performance of these robust statistics have been analyzed in simulation and compared to the one associated with standard multivariate normal assumption. When the data is heterogeneous, the detection and estimation strategy yields better results with the new statistics.
机译：在本文中，我们研究了在多元图像时间序列中检测和估计变化点的问题。我们扩展现有工作以考虑空间邻域上数据集的异质性。数据的经典复杂高斯假设被复杂的椭圆对称假设取代。然后，使用广义似然比检验（GLRT）得出可靠的统计数据。这些统计信息与一个或多个更改的估计策略结合在一起。这些稳健统计数据的性能已在仿真中进行了分析，并与标准多元正态假设相关联的性能进行了比较。当数据是异构的时，新的统计信息将为检测和估计策略带来更好的结果。

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