一种基于信息论的多散射体检测器用于合成孔径雷达成像
An Information-Theoretic Detector for Multiple Scatterers in SAR Tomography
AI总结：
本文提出了一种基于信息论的多散射体检测方法，结合自适应架构和压缩感知技术，用于SAR成像中的多散射体检测与参数估计。
摘要：
持久散射体干涉测量和合成孔径雷达（SAR）成像是检测和时间监测持久散射体的强大工具。它们已被证明在城市场景中非常有效，特别是用于建筑物和基础设施的三维重建和形变监测。在城市地区，叠加现象会导致同一图像像素内来自不同高度散射体的多重贡献。在SAR成像的背景下，这个问题可以通过考虑多重假设检验来检测可行的多散射体[1][2]。在本文中，我们从信息论的角度考虑这个问题，并利用[3]中开发的理论工具，设计了一种适用于SAR成像中多重假设检验问题的一阶段自适应架构。此外，我们还采用压缩感知方法来估计每个假设下的未知参数。该架构已在模拟数据和真实数据上进行了验证，并与合适的替代方法进行了比较。
英文摘要：
Persistent scatterer interferometry and Synthetic Aperture Radar (SAR) Tomography are powerful tools for the detection and time monitoring of persistent scatterers. They have been proven to be effective in urban scenarios, especially for buildings and infrastructures 3-D reconstruction and monitoring of deformation. In urban areas, occurrence of layover leads to the presence of multiple contributions within the same image pixel from scatterers located at different heights. In the context of SAR Tomography, this problem can be addressed by considering a multiple hypothesis test to detect the presence of feasible multiple scatterers [1][2]. In the present paper, we consider this problem in the framework of the information theory and exploit the theoretical tool, developed in [3], to design a one-stage adaptive architecture for multiple hypothesis testing problems in the context of SAR Tomography. Moreover, we resort to the compressive sensing approach for the estimation of the unknown parameters under each hypothesis. This architecture has been verified on both simulated as well as real data also in comparison with suitable counterparts.
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