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具有随机粗糙分形界面的多层介质散射微扰理论:遥感应用

Perturbation Theory for Scattering from Multilayers with Randomly Rough Fractal Interfaces: Remote Sensing Applications.

作者信息

Imperatore Pasquale, Iodice Antonio, Riccio Daniele

机构信息

Istituto per il Rilevamento Elettromagnetico dell'Ambiente, Consiglio Nazionale delle Ricerche (CNR), 80124 Napoli, Italy.

Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università di Napoli Federico II, 80125 Napoli, Italy.

出版信息

Sensors (Basel). 2017 Dec 27;18(1):54. doi: 10.3390/s18010054.

Abstract

A general, approximate perturbation method, able to provide closed-form expressions of scattering from a layered structure with an arbitrary number of rough interfaces, has been recently developed. Such a method provides a unique tool for the characterization of radar response patterns of natural rough multilayers. In order to show that, here, for the first time in a journal paper, we describe the application of the developed perturbation theory to fractal interfaces; we then employ the perturbative method solution to analyze the scattering from real-world layered structures of practical interest in remote sensing applications. We focus on the dependence of normalized radar cross section on geometrical and physical properties of the considered scenarios, and we choose two classes of natural stratifications: wet paleosoil covered by a low-loss dry sand layer and a sea-ice layer above water with dry snow cover. Results are in accordance with the experimental evidence available in the literature for the low-loss dry sand layer, and they may provide useful indications about the actual ability of remote sensing instruments to perform sub-surface sensing for different sensor and scene parameters.

摘要

最近开发了一种通用的近似微扰方法,该方法能够给出具有任意数量粗糙界面的分层结构散射的闭式表达式。这种方法为表征天然粗糙多层的雷达响应模式提供了一个独特的工具。为了证明这一点,在本文中,我们首次在期刊论文中描述了所开发的微扰理论在分形界面中的应用;然后,我们采用微扰方法解来分析实际遥感应用中具有实际意义的真实世界分层结构的散射。我们关注归一化雷达截面与所考虑场景的几何和物理特性的相关性,并选择两类自然分层:被低损耗干砂层覆盖的湿古土壤以及水上有干雪覆盖的海冰层。结果与文献中关于低损耗干砂层的实验证据一致,并且它们可能为不同传感器和场景参数下遥感仪器进行地下传感的实际能力提供有用的指示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95c0/5795758/8c17c619613f/sensors-18-00054-g001.jpg

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