A signal processing view of strip-mapping synthetic aperture radar

Munson, D.C.; Visentin, R.

doi:10.1109/29.45556

Cited by 112 publications

(48 citation statements)

References 34 publications

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“…By minimizing the Bayes risk, we obtain two Bayes-optimal decision rules: one Neyman-Pearson-type decision rule for detection of canonical targets in clutter and one maximum a posteriori (MAP) type rule for classification of detected canonical forms. The hypotheses are (22) i=1 (23) For the clutter signal we adopt an additive, spherically invariant random vector (SIRV) model, which includes Gaussian, K, log-normal and Weibull clutter probability distributions as special cases [66]. The complex clutter random vector is the product of a positive real amplitude with a complex Gaussian random vector .…”

Section: B Glrt Processingmentioning

confidence: 99%

“…The and mechanisms are separated by only 0.0008 Fourier Equation (4) represents a general scattering model, and forms the basis of all parametric scattering models we consider. In many applications, the radar measurements are sampled on a polar grid [23] (6) It is desirable in many cases to consider scattered field measurements sampled on a rectilinear grid. The primary reason is that the phase term in (4) or (5) is nonlinear on a polar grid, but becomes linear on a rectilinear grid.…”

Section: A Scattering Modelmentioning

confidence: 99%

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Attributed scattering centers for SAR ATR

Potter

Moses

1997

IEEE Trans. on Image Process.

439

210

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Abstract-High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behavior predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides highresolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a leastsquares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.

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Section: B Glrt Processingmentioning

confidence: 99%

Section: A Scattering Modelmentioning

confidence: 99%

Attributed scattering centers for SAR ATR

Potter

Moses

1997

IEEE Trans. on Image Process.

439

210

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“…Synthetic aperture radar sensors can produce range imagery of high spatial resolution under di cult weather conditions (Munsen, O'Brien, andJenkins, 1983 Munsen andVisentin, 1989) but the image data presents some di culties for interpretation by h uman observers or automatic recognition systems. Among these di culties is the large dynamic range ( ve orders of magnitude) of the sensor signal (see Figures 1a and 2a), which requires some type of nonlinear compression merely for an image to be represented and viewed on a typical computer monitor.…”

Section: Introductionmentioning

confidence: 99%

Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation

1995

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A neural network model of boundary segmentation and surface representation is developed to process images containing range data gathered by a s y n thetic aperture radar (SAR) sensor. The boundary and surface processing are accomplished by an improved Boundary Contour System (BCS) and Feature Contour System (FCS), respectively, t h a t h a ve been derived from analyses of perceptual and neurobiological data. BCS/FCS processing makes structures such as motor vehicles, roads, and buildings more salient and interpretable to human observers than they are in the original imagery. Early processing by ON cells and OFF cells emb e d d e d i n s h unting centersurround network models preprocessing by lateral geniculate nucleus (LGN). Such preprocessing compensates for illumination gradients, normalizes input dynamic range, and extracts local ratio contrasts. ON cell and OFF cell outputs are combined in the BCS to de ne oriented lters that model cortical simple cells. Pooling ON and OFF outputs at simple cells overcomes complementary processing de ciencies of each c e l l t ype along concave and convex contours, and enhances simple cell sensitivity to image edges. Oriented lter outputs are recti ed and outputs sensitive to opposite contrast polarities are pooled to de ne complex cells. The complex cells output to stages of shortrange spatial competition (or endstopping) and orientational competition among hypercomplex cells. Hypercomplex cells activate long range cooperative bipole cells that begin to group image boundaries. Nonlinear feedback b e t ween bipole cells and hypercomplex cells segments image regions by cooperatively completing and regularizing the most favored boundaries while suppressing image noise and weaker boundary groupings. Boundary segmentation is performed by three copies of the BCS at small, medium, and large lter scales, whose subsequent i n teraction distances covary with the size of the lter. Filling-in of multiple surface representations occurs within the FCS at each scale via a boundary-gated di usion process. Di usion is activated by the normalized LGN ON and OFF outputs within ON and OFF lling-in domains. Di usion is restricted to the regions de ned by gating signals from the corresponding BCS boundary segmentation. The lled-in opponent O N and OFF signals are subtracted to form double opponent surface representations. These surface representations are shown by a n y of three methods to be sensitive to both image ratio contrasts and background luminance. The three scales of surface representation are then added to yield a nal multiple-scale output. The BCS and FCS are shown to perform favorably in comparison to several other techniques for speckle removal.

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“…What remains then is a horizontal recursion in which these q estimates are successively merged to form the desired estimates £(slY'Yi ) , i -1, 2, · · q: Equations (16) - (17) for computing these merged estimates follow from the fact that the measurements in the sets Y,,,i, i = 1, 2,... , q are conditionally independent given z(s). Thus we see that, as compared to the merge step (9) -(10) for the Kalman filter, the merge step for likelihood calculation involves a q-step horizontal recursion (16), (17) in which the last step yields the same quantity The upward update-predict-merge process continues up the tree until the root node is reached.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Likelihood Calculation for a Class of Multiscale Stochastic Models, with Application to Texture Discrimination

Luettgen¹,

Willsky²

1993

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A class of multiscale stochastic models based on scale-recursive dynamics on trees has recently been introduced. Theoretical and experimental results have shown that these models provide an extremely rich framework for representing both processes which are intrinsically multiscale, e.g., 1/f processes, as well as 1-D Markov processes and 2-D Markov random fields. Moreover, efficient optimal estimation algorithms have been developed for these models by exploiting their scale-recursive structure. In this paper, we exploit this structure in order to develop a computationally efficient and parallelizable algorithm for likelihood calculation. We illustrate one possible application to texture discrimination and demonstrate that likelihood-based methods using our algorithm have substantially better probability of error characteristics than well-known least-squares methods, and achieve performance comparable to that of Gaussian Markov random field based techniques, which in general are prohibitively complex computationally.

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A signal processing view of strip-mapping synthetic aperture radar

Cited by 112 publications

References 34 publications

Attributed scattering centers for SAR ATR

Attributed scattering centers for SAR ATR

Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation

Likelihood Calculation for a Class of Multiscale Stochastic Models, with Application to Texture Discrimination

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