Tensor Tomographic Imaging for Foliage Penetration (FOPEN)

树叶穿透张量断层成像 (FOPEN)

• 批准号: 2905448
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2905448
• 关键词: Tensor; Tomographic; Imaging; Foliage; Penetration

The main aim of this PhD is to develop the tools that are fundamental in resolving the challenges faced by Radar Imaging, especially in a forestry environment. For instance, how would we alleviate the non-linear effects of Electromagnetic wave scattering due to objects of interests in Synthetic Aperture Radar (SAR) images? Developments in more accurate sensor systems coupled with the increase in computational power for portable devices give rise to an explosion in the hardware to capture rich datasets.Low Frequency Electromagnetic waves penetrate foliage better, but result in lower resolution images using known image formation algorithms. Furthermore, the received multi-path/bounce of Electromagnetic waves scattered by anisotropic objects adds complexity in imaging areas of dense vegetation. It is envisaged that a rich data structure of multi-polarisation, multi-frequency, multi-look and multi-static geometry will ease in the process of acquiring a volumetric image of the region of interest with an enhanced description of the scene, i.e. tensor tomography. Due to the effects of Electromagnetic waves in a foliage environment, the retrieval of a high resolution image is classed as a nonlinear Inverse Problem, specifically as Inverse Boundary Value Problems (BVPs) for Maxwell's equations.

该博士学位的主要目的是开发在解决雷达成像所面临的挑战方面至关重要的工具，特别是在林业环境中。例如，我们将如何减轻电磁波散射的非线性效应，由于目标的合成孔径雷达（SAR）图像？更精确的传感器系统的发展加上便携式设备的计算能力的增加引起了硬件的爆炸，以捕获丰富的数据集。低频电磁波更好地穿透树叶，但使用已知的图像形成算法会导致分辨率较低的图像。此外，接收到的由各向异性物体散射的电磁波的多径/反弹增加了密集植被的成像区域的复杂性。可以设想，多极化、多频率、多视和多静态几何结构的丰富数据结构将在采集具有场景的增强描述的感兴趣区域的体积图像的过程中变得容易，即张量断层摄影。由于电磁波的影响，高分辨率图像的检索被归类为非线性反问题，具体地说是麦克斯韦方程的逆边值问题（BVP）。