Northrop Grumman Corporation, Electronic Sensors & Systems Division, Baltimore, MD 21203, USA.
IEEE Trans Image Process. 1998;7(5):729-61. doi: 10.1109/83.668029.
This paper discusses the use of modern 2D spectral estimation algorithms for synthetic aperture radar (SAR) imaging. The motivation for applying power spectrum estimation methods to SAR imaging is to improve resolution, remove sidelobe artifacts, and reduce speckle compared to what is possible with conventional Fourier transform SAR imaging techniques. This paper makes two principal contributions to the field of adaptive SAR imaging. First, it is a comprehensive comparison of 2D spectral estimation methods for SAR imaging. It provides a synopsis of the algorithms available, discusses their relative merits for SAR imaging, and illustrates their performance on simulated and collected SAR imagery. Some of the algorithms presented or their derivations are new, as are some of the insights into or analyses of the algorithms. Second, this work develops multichannel variants of four related algorithms, minimum variance method (MVM), reduced-rank MVM (RRMVM), adaptive sidelobe reduction (ASR) and space variant apodization (SVA) to estimate both reflectivity intensity and interferometric height from polarimetric displaced-aperture interferometric data. All of these interferometric variants are new. In the interferometric contest, adaptive spectral estimation can improve the height estimates through a combination of adaptive nulling and averaging. Examples illustrate that MVM, ASR, and SVA offer significant advantages over Fourier methods for estimating both scattering intensity and interferometric height, and allow empirical comparison of the accuracies of Fourier, MVM, ASR, and SVA interferometric height estimates.
本文讨论了现代二维谱估计算法在合成孔径雷达（SAR）成像中的应用。将功率谱估计方法应用于 SAR 成像的动机是为了提高分辨率、消除旁瓣伪影、减少斑点与传统的傅里叶变换 SAR 成像技术相比。本文对自适应 SAR 成像领域做出了两个主要贡献。首先，它对 SAR 成像的二维谱估计方法进行了全面比较。它概述了可用的算法，讨论了它们在 SAR 成像中的相对优点，并在模拟和采集的 SAR 图像上展示了它们的性能。提出的一些算法或其推导是新的，对算法的一些见解或分析也是新的。其次，这项工作开发了四种相关算法的多通道变体，最小方差法（MVM）、降秩 MVM（RRMVM）、自适应旁瓣抑制（ASR）和空间变迹（SVA），从极化移变孔径干涉数据中估计反射率强度和干涉高度。所有这些干涉变体都是新的。在干涉竞争中，自适应谱估计可以通过自适应消零和平均的组合来提高高度估计。示例说明了 MVM、ASR 和 SVA 在估计散射强度和干涉高度方面比傅里叶方法具有显著优势，并允许对傅里叶、MVM、ASR 和 SVA 干涉高度估计的准确性进行经验比较。
