The aim of this paper is to provide and numerically test in the presence of measurement noise a procedure for target classification in wave imaging based on comparing frequency-dependent distribution descriptors with precomputed ones in a dictionary of learned distributions. Distribution descriptors for inhomogeneous objects are obtained from the scattering coefficients. First, we extract the scattering coefficients of the (inhomogeneous) target from the perturbation of the reflected waves. Then, for a collection of inhomogeneous targets, we build a frequency-dependent dictionary of distribution descriptors and use a matching algorithm in order to identify a target from the dictionary up to some translation, rotation and scaling.

中文翻译：

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非均匀物体的散射系数及其在波浪成像目标分类中的应用

本文的目的是在存在测量噪声的情况下提供和数值测试波浪成像中目标分类的过程，该过程基于将频率相关分布描述符与学习分布字典中的预先计算的分布描述符进行比较。非均匀对象的分布描述符是从散射系数中获得的。首先，我们从反射波的扰动中提取（非均匀）目标的散射系数。然后，对于一组不均匀的目标，我们构建了一个与频率相关的分布描述符字典，并使用匹配算法从字典中识别目标，直至进行一些平移、旋转和缩放。