Huynen-Euler (H-E) parameters proposed based on the diagonalization of the scattering matrix are of significant importance for single target information interpretation because of their explicit physical meanings. However, their application in target classification/recognition seems unsuccessful hitherto. Besides, the process of extracting the five H-E parameters by existing approaches, i.e., eigen-decomposition and unitary transformation, is a bit tedious and relatively time consuming, especially for large-sized polarimetric synthetic aperture radar (PolSAR) data. In this article, new H-E parameters are proposed to improve the performance of H-E decomposition in describing the scattering characteristics of the target. Furthermore, a fast decomposition approach is presented to derive the parameters directly and simultaneously with high computational efficiency. Another advantage of the fast H-E decomposition (FHED) is that, different from the original algorithm, which can only be applied to the single target, FHED is also effective for distributed targets, which expands the application range of the H-E decomposition. Experimental results on the temporal PolSAR data show that the changes in the disaster-affected area can be detected according to the changes in the newly proposed parameters, and the degree of change in the newly proposed skip angle has a linear relationship with the degree of urban damage. This indicates that FHED has a good prospect in disaster monitoring, especially for the estimation of building damage level (DL). It is also proved that the building DL mapped by the new skip angle and by the double-bounce scattering power, the most widely used parameter for such a situation, are highly consistent, while the former consumes much less time. Therefore, FHED can be applied to disaster monitoring and damage detection effectively, which is conducive to rescue operations by providing important information with a quick response.

中文翻译：

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快速Huynenuler分解及其在灾害监测中的应用


基于散射矩阵对角化提出的惠南-欧拉（HE）参数因其明确的物理意义对于单目标信息解释具有重要意义。然而，它们在目标分类/识别中的应用迄今为止似乎并不成功。此外，现有方法（即特征分解和酉变换）提取五个HE参数的过程有点繁琐且相对耗时，特别是对于大尺寸极化合成孔径雷达（PolSAR）数据。本文提出了新的HE参数来提高HE分解在描述目标散射特性方面的性能。此外，提出了一种快速分解方法，以高计算效率直接同时导出参数。快速HE分解（FHED）的另一个优点是，与原始算法只能应用于单个目标不同，FHED对于分布式目标也有效，这扩大了HE分解的应用范围。在时态PolSAR数据上的实验结果表明，根据新提出的参数的变化可以检测到受灾区域的变化，并且新提出的跳跃角的变化程度与城市化程度呈线性关系。损害。这表明FHED在灾害监测方面具有良好的前景，特别是在建筑物损坏程度（DL）的估计方面。还证明了通过新的跳跃角度和双反射散射功率（这种情况下使用最广泛的参数）映射的建筑物深度学习具有高度一致性，而前者消耗的时间要少得多。 因此，FHED可以有效地应用于灾害监测和损害检测，通过提供重要信息和快速响应，有利于救援行动。