Detecting and delineating hot spots in data from radiation sensors is required in applications ranging from monitoring large geospatial areas to imaging small objects in close proximity. This paper describes a computational method for localizing potential hot spots in matrices of independent Poisson data where, in numerical terms, a hot spot is a cluster of locally higher sample mean values (higher Poisson intensity) embedded in lower sample mean values (lower background intensity). Two numerical algorithms are computed sequentially for a 3D array of 2D matrices of gross Poisson counts: (1) nonnegative tensor factorization of the 3D array to maximize a Poisson likelihood and (2) phase congruency in pertinent matrices. The indicators of potential hot spots are closed contours in phase congruency in these matrices. The method is illustrated for simulated Poisson radiation datasets, including visualization of the phase congruency contours. The method may be useful in other applications in which there are matrices of nonnegative counts, provided that a Poisson distribution fits the dataset.

在从监测大型地理空间区域到近距离成像小物体的应用中，需要检测和描绘来自辐射传感器的数据中的热点。本文描述了一种用于在独立泊松数据矩阵中定位潜在热点的计算方法，在数值方面，热点是嵌入较低样本平均值（较低背景强度）的局部较高样本平均值（较高泊松强度）的集群）。两种数值算法按顺序计算用于 3D 总泊松计数矩阵的 3D 阵列：(1) 3D 阵列的非负张量分解以最大化泊松似然和 (2) 相关矩阵中的相位一致性。潜在热点的指标是这些矩阵中相位一致的闭合轮廓。该方法针对模拟的泊松辐射数据集进行了说明，包括相位一致性轮廓的可视化。如果 Poisson 分布拟合数据集，该方法可能在存在非负计数矩阵的其他应用程序中很有用。