This paper demonstrates that synthetic aperture radar (SAR) images formed using modern spectral estimates can be more accurately segmented than traditional SAR images. Classical FFT based Fourier image formation algorithms produce imagery with strong speckle and sidelobe artifacts that hinder the segmentation process. We show that imagery formed using Capon's minimum variance spectral estimate changes the statistics of the SAR imagery in a way that increases the separation between the various classes of natural terrain. The increased class separation leads to more accurate segmentation. We use the MSTAR dataset to show the statistical changes and demonstrate the improvement in segmentation relative to Fourier imagery.

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.

The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data. By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio. This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation. Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise. Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation. ASR performance characteristics can be varied through the choice of filter order, l(1)- or l(2)-norm filter vector constraints and a separable or nonseparable multidimensional implementation. The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery.

The exact equations governing the narrow-band resolution and detection capabilities of conventional (CONV) and minimum energy (ME) adaptive beamforming [1], are the basis for a class of linear arrays which offers simultaneously optimum resolution and detection capacities. Signal processing considerations indicate that, in this class, linear minimum redundancy (LMR) arrays [2] are best suited to CONV beamforming, and linear minimum hole (LMH) arrays [3] are best suited to minimum energy (ME) beamforming. The effect of coarray redundancies and holes on sidelobe level is examined analytically, and lower bounds are established on the number of coarray redundancies and holes. A simple recursive algorithm for the design of LMH arrays is presented.

The effect of source coherence on the capabilities of classical, minimum energy (ME), and linear predictive (LP) array processing algorithms to estimate the bearings of two equal-energy sources is examined. Source coherence is shown to adversely affect the resolution and detection capabilities, as well as the bias characteristics, of all three algorithms. For linear arrays of equally-spaced sensors (LES arrays), the superior resolution capability of the LP algorithm is demonstrated. However, the LP algorithm is least capable of detecting highly coherent sources. The estimates of source bearing produced by each algorithm are shown to be asymptotically biased.