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Within the hyperspectral community, change detection is a continued area of interest. Interesting changes in imagery typically correspond to changes in material reflectance associated with pixels in the scene. Using a physical model describing the sensor-reaching radiance, change detection can be formulated as a statistical hypothesis test. Complicating the problem of change detection is the presence of shadow, illumination, and atmospheric differences, as well as misregistration and parallax error, which often produce the appearance of change. The proposed physical model incorporates terms to account for both direct and diffuse shadow fractions to help mitigate false alarms associated with shadow differences between scenes. The resulting generalized likelihood ratio test (GLRT) provides an indicator of change at each pixel. The maximum likelihood estimates of the physical model parameters used for the GLRT are obtained from the entire joint data set to take advantage of coupled information existing between pixel measurements. Simulation results using synthetic and real imagery demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

We consider the problem of joint enhancement of multichannel images with pixel based constraints on the multichannel data. Previous work by Çetin and Karl introduced nonquadratic regularization methods for SAR image enhancement using sparsity enforcing penalty terms. We formulate an optimization problem that jointly enhances complex-valued multichannel images while preserving the cross-channel information, which we include as constraints tying the multichannel images together. We pose this problem as a joint optimization problem with constraints. We first reformulate it as an equivalent (unconstrained) dual problem and develop a numerically-efficient method for solving it.We develop the Dual Descent method, which has low complexity, for solving the joint optimization problem. The algorithm is applied to both an interferometric synthetic aperture radar (IFSAR) problem, in which the relative phase between two complex-valued images indicate height, and to a synthetic multimodal medical image example. [ABSTRACT FROM AUTHOR]

We propose a model for generating low-frequency synthetic aperture radar (SAR) clutter that relates model parameters to physical characteristics of the scene. The model includes both distributed scattering and large-amplitude discrete clutter responses. The model also incorporates the SAR imaging process, which introduces correlation among image pixels. The model may be used to generate synthetic clutter for a range of environmental operating conditions for use in target detection performance evaluation of the radar and automatic target detection/recognition algorithms. We derive a statistical representation of the proposed clutter model's pixel amplitudes and compare with measured data from the CARABAS.II SAR. Simulated clutter images capture the structure and amplitude responses seen in the measured data. A statistical analysis shows an order of magnitude improvement in model fit error compared with standard maximum-likelihood (ML) density fitting methods. [ABSTRACT FROM AUTHOR]

We consider characterization of building interior structure from two-pass interferometric circular synthetic aperture radar data. We model returns from complex building structures as the sum of the responses from scattering primitives—plates, dihedrals, and trihedrals—observed through transfer functions that characterize both the transmission through and reflection from dielectric surfaces. Maximum-likelihood estimates for the location of primitive features and wall propagation! reflection parameters are obtained through a two-stage detection-and-estimation algorithm. We employ a sparse reconstruction algorithm to detect primitive signatures in the presence of wall effects and refine our estimates using a nonlinear minimization of local cost functions. We show that using simplified wall and primitive models, we can extract geometrically relevant features which can be combined to reveal 3-D floorplan estimates of buildings. Examples illustrate the effectiveness of the feature extraction approach. [ABSTRACT FROM AUTHOR]

This paper discusses the effect of motion measurement errors (MMEs) on measured bistatic synthetic aperture radar (SAR) phase history data that has been motion compensated to the scene origin. We characterize the effect of low-frequency MMEs on bistatic SAR images, and, based on this characterization, we derive limits on the allowable MMEs to be used as system specifications. Finally, we demonstrate that proper orientation of a bistatic SAR image during the image formation process allows application of monostatic SAR autofocus algorithms in postprocessing to mitigate image defocus. [ABSTRACT FROM AUTHOR]

This paper discusses reconstruction of three-dimensional surfaces from multiple bistatic synthetic aperture radar (SAR) images. Techniques for surface reconstruction from multiple monostatic SAR images already exist, including interferometric processing and stereo SAR. We generalize these methods to obtain algorithms for bistatic interferometric SAR and bistatic stereo SAR. We also propose a framework for predicting the performance of our multistatic stereo SAR algorithm, and, from this framework, we suggest a metric for use in planning strategic deployment of multistatic assets. [ABSTRACT FROM AUTHOR]

The polar format algorithm (PFA) for spotlight synthetic aperture radar (SAR) is based on a linear approximation for the differential range to a scatterer. We derive a second-order Taylor series approximation of the differential range. We provide a simple and concise derivation of both the far-field linear approximation of the differential range, which forms the basis of the PFA, and the corresponding approximation limits based on the second-order terms of the approximation. [ABSTRACT FROM AUTHOR]

Matched filtering (MIF) of phase history data is a mathematically ideal but computationally expensive approach to bistatic synthetic aperture radar (SAR) image formation. Fast backprojection algorithms (BPAs) for image formation have recently been shown to give improved O(N2 log2 N) performance. In this paper, an O(N2 log2 N) bistatic polar format algorithm (PFA) based on a bistatic far-field assumption is derived. This algorithm is a generalization of the popular PFA for monostatic SAR image formation and is highly amenable to implementation with existing monostatic image formation processors. Limits on the size of an imaged scene, analogous to those in monostatic systems, are derived for the bistatic PFA. [ABSTRACT FROM AUTHOR]

We present a recursive backprojection-based procedure for synthetic aperture imaging. In traditional synthetic aperture radar (SAR) imaging, radar measurements are processed in blocks of contiguous azimuth angle measurements. In continuous imaging applications, block processing generally entails significant computational overhead, in part due to overlapped processing of azimuth intervals. We consider a novel recursive update approach, in which the SAR image is updated using an autoregressive-based algorithm as new measurements are taken. In this approach, the SAR image is found as a linear combination of a small number of previous images and a term containing the current radar measurement. The computational and memory requirements of the recursive imaging approach are modest—on the order of M\cdot N^2, where M is the recursion order (typically \leq3) and N^2 is the image size. In addition, we investigate the crossrange sidelobes realized by the autoregressive (AR) imaging approach. We show that a first-order autoregression of the image gives crossrange sidelobes similar to a rectangular azimuth window, while a third-order autoregression gives sidelobes comparable to those obtained from widely-used windows in block-processing image formation. We compare images obtained from the recursive and block-processing techniques, both for a synthetic scene and for X-band SAR measurements from the Gotcha data set. [ABSTRACT FROM AUTHOR]