We investigate the use of direct-Fourier (DF) image reconstruction in computerized tomography and synthetic aperture radar (SAR). One of our aims is to determine why the convolutionbackprojection (CBP) method is favored over DF methods in tomography, while DF methods are virtually always used in SAR. We show that the CBP algorithm is equivalent to DF reconstruction using a Jacobian-weighted 2-D periodic sinc-kernel interpolator. This interpolation is not optimal in any sense, which suggests that DF algorithms utilizing optimal interpolators may surpass CBP in image quality. We consider use of two types of DF interpolation: a windowed sinc kernel, and the least-squares optimal Yen interpolator. Simulations show that reconstructions using the Yen interpolator do not possess the expected visual quality, because of regularization needed to preserve numerical stability. Next, we show that with a concentric-squares sampling scheme, DF interpolation can be performed accurately and efficiently...

reflected TV signals. UHF-band TSAR imaging requires wide-angle data to produce good cross-range resolution. We show that direct Fourier reconstruction (DFR) causes degradation of the reconstructed image due to aspect-dependent scattering. We find that a Smoothed Pseudo Wigner-Ville distribution (SPWVD) applied in the cross-range direction in place of the Fourier transform can generate a sequence of images, which shows the target reflectivity as a function of aspect angle. Compared to DFR results, these images have higher cross-range resolution. A final image can be synthesized from these images and used for target recogni- tion. XPATCH is used to simulate monostatic data from an aircraft. The proposed SPWVD-based imaging method produces a useful image of the aircraft from this data.

This paper also proposed another reconstruction method based on a direct approximation of the Fourier inversion formula using a twodimensional (2-D) trapezoidal rule. In addition, the possibility of reconstruction from a concentric-squares raster was discussed. Numerous simple interpolators have been tried in DF reconstruction with the results compared with CBP [33]. In [34] and [35], the concept of angular bandlimiting was used to interpolate the polar data onto a Cartesian grid. In [36], a DF reconstruction using bilinear interpolation for diffraction tomography provided image quality that was comparable to that produced by the CBP algorithm. Very good reconstruction quality was obtained in [37] and [38] using a spline interpolator, or a hybrid type of spline interpolator. The notion of "gridding" was introduced in [39] as a method of obtaining optimal inversion of Fourier data. An optimal gridding function was proposed, and successful results were obtained when applied to the tomographic reconstruction problem. In [40], several different gridding functions were tried for DF reconstruction, and the performances were compared. In [41, 42], the linogram reconstruction method was proposed as a form of DF reconstruction. The data collection grid in the linogram method is the same as in the concentric-squares sampling scheme. The inversion of the Fourier data in [41, 42] was accomplished by first applying the chirp-z transform in one direction and then computing FFTs in the other direction. In CT, many of these attempts at DF reconstruction have given a poorer result than the CBP algorithm, due to the error incurred in the process of the polar-to-Cartesian interpolation. The attraction of DF reconstruction, however, is that it is thought to require less computation than ...

One of the biggest challenges for automatic target recognition (ATR) methods is the accurate and efficient extraction of features from synthetic aperture radar (SAR) images. We have recently developed a new SAR image formation technique which is recognition-oriented in the sense that it enhances features in the scene which we believe are important for recognition purposes. In this paper, we evaluate the performance of the images produced by this technique in terms of preserving and enhancing these features. The findings of our analysis indicate that the new SAR image formation method provides images with higher resolution of scatterers, and better separability of different regions as compared to conventional SAR images.

Sandia is a multiprogram laboratory operated by Sandia Corporation,

In underwater sensing applications it is often difficult to train a classifier in advance for all targets that may be seen during testing, due to the large number of targets that may be encountered. We therefore partition the training data into target classes, with each class characteristic of multiple targets that share similar scattering physics. In some cases one may have a priori insight into which targets should constitute a given class, while in other cases this segmentation must be done autonomously based on the scattering data. For the latter case, we constitute the classes using an information-theoretic mapping criterion. Having defined the target classes, the second phase of our identification procedure involves determining those features that enhance the similarity between the targets in a given class. This is achieved by using a GA-based feature selection algorithm, with a Kullback-Leibler (KL) cost function. The classifier employed is appropriate for multi-aspect scattering data and is based on a hidden Markov model (HMM). The performance of the class-based classification algorithm is examined using both measured and computed acoustic scattering data from submerged elastic targets.

We propose a fast algorithm for far-field SAR imaging based on a new fast back-projection algorithm developed for tomography. We also modify the algorithm for the near-field scenario. The fast back-projection algorithm for SAR has computational complexity O(N²log N). Compared to traditional FFT-based methods, our new algorithm has potential advantages: the new algorithm does not need frequency-domain interpolation, which becomes complex for the wide-angle case; the new approach is applicable to the near-field scenario, taking into account wavefront curvature; and the back-projection algorithm can be easily adapted to parallel computing architectures. For some scenarios of interest, the computational cost of the new backprojection approach is similar to or less than that for FFT-based algorithms.

Abstract not found

We present an evaluation of the impact of a recently proposed synthetic aperture radar (SAR) imaging technique on feature enhancement and automatic target recognition (ATR) performance. This image formation technique is based on non-quadratic optimization, and the images it produces appear to exhibit enhanced features. In the first part of this paper, we quantify such feature enhancement through a number of criteria. The findings of our analysis indicate that the new feature-enhanced SAR image formation method provides images with higher resolution of scatterers, and better separability of different regions as compared to conventional SAR images. In the second part of this paper, we provide an ATRbased evaluation. We run recognition experiments using conventional and feature-enhanced SAR images of military targets, with three different classifiers. The first classifier is template-based. The second classifier makes a decision through a likelihood test, based on Gaussian models for reflectivities. The third classifier is based on extracted locations of the dominant target scatterers. The experimental results demonstrate that the new feature-enhanced SAR imaging method can improve the recognition performance, especially in scenarios involving reduced data quality or quantity.

We present a new non-iterative approach to synthetic aperture radar (SAR) autofocus, termed the MultiChannel Autofocus (MCA) algorithm. The key in the approach is to exploit the multichannel redundancy of the defocusing operation to create a linear subspace, where the unknown perfectly-focused image resides, expressed in terms of a known basis formed from the given defocused image. A unique solution for the perfectlyfocused image is then directly determined through a linear algebraic formulation by invoking an additional image support condition. The MCA approach is found to be computationally efficient and robust, and does not require prior assumptions about the SAR scene used in existing methods. In addition, the vector-space formulation of MCA allows sharpness metric optimization to be easily incorporated within the restoration framework as a regularization term. We present experimental results characterizing the performance of MCA in comparison with conventional autofocus methods, and discuss the practical implementation of the technique.