Comparison of continuous and discrete mixed-integrator processors

Zelenka, J. S.

doi:10.1364/josa.66.001295

Cited by 34 publications

(7 citation statements)

References 8 publications

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“…Furthermore, image speckle (which modulates the backscatter cross sections in the received signal) is uncorrelated between the two frequencies because the spectra corresponding to the two data takes do not overlap, resulting in a product of the spectra equal to zero, that is, uncorrelated data. It is for the same reason that multilooking based on spectral division 4 works for reducing image speckle. In effect, the one-look spectra selected for multilooking do not overlap, resulting in independent sample images or looks of the scene.…”

Section: Generalization To the Multifrequency Casementioning

confidence: 99%

“…A computationally convenient alternative is to adapt the number of looks N in Eqs. (1) and (2) (4) where we used a fundamental property of zero-mean circular symmetric Gaussian processes in the third equality, and the intensities I,(s), with p E {1,...,N}, and Iq(s + r), with q E {1,.. , N}, are the one-look intensities used to form the N-look intensities I, and IS+r, respectively. The corresponding one-look complex amplitudes a, and aq are such that (5) where dx is an elementary displacement in the image plane and hp is the product of convolution of the system coherent impulse response h with the multilooking filter fp used to select the one-look complex sample a, from the available SAR bandwidth.…”

Section: Marginal Distributionsmentioning

confidence: 99%

“…correlation of the one-look complex samples used to form the multilook image by using an expression given by Zelenka. 4 The incidence angle Oi varies between 220 (near range, top in Fig. 2) and 520 (far range, bottom in Fig.…”

Section: Adaptation To Incidence Angle Effectsmentioning

confidence: 99%

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Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

Rignot

Chellappa

1993

J. Opt. Soc. Am. A

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We present a maximum a posteriori (MAP) classifier for classifying multifrequency, multilook, single polarization, synthetic aperture radar (SAR) intensity data into regions or ensembles of pixels of homogeneous and similar radar backscatter characteristics. A model for the prior joint distribution of the multifrequency SAR intensity data is combined with a Markov random field for representing the interactions between region labels to obtain an expression for the posterior distribution of the region labels given the multifrequency SAR observations. The maximization of the posterior distribution yields Bayes's optimum region labeling or classification of the SAR data or its MAP estimate. The performance of the MAP classifier is evaluated by using computersimulated multilook SAR intensity data as a function of the parameters in the classification process. Multilook SAR intensity data are shown to yield higher classification accuracies than one-look SAR complex amplitude data. Examples using actual two-frequency, four-look, SAR intensity data acquired by the NASA/Jet Propulsion Laboratory airborne polarimetric SAR are presented. The MAP classifier is extended to the case in which the radar backscatter from the remotely sensed surface varies within the SAR image because of incidence angle effects. The results obtained illustrate the practicality of the method for combining SAR intensity observations acquired at two different frequencies and for improving classification accuracy of SAR data.

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Section: Generalization To the Multifrequency Casementioning

confidence: 99%

Section: Marginal Distributionsmentioning

confidence: 99%

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Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

Rignot

Chellappa

1993

J. Opt. Soc. Am. A

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“…Zelenka (1976) discusses the mathematics supporting a discrete and a scanning system for SAR speckle reduction. As in many other discussions, the averaging is accomplished by processing subapertures coherently and then adding the resulting images on an intensity basis.…”

Section: Radar Speckle and Multiplicative Noise Literaturementioning

confidence: 99%

Radar Image Interpretability Analysis.

Frost¹,

Stiles²,

Holtzman³

1981

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“…In the optics literature there has been an interest in bounds of the latter type. Here one should think of point-spread functions F of illuminances with associated transfer functions f (see, for instance, [4][5][6][7][8][9][10][11][12] and the survey paper [13]). Also in the field of network analysis bounds of the latter type have received attention; see [14,15].…”

Section: Introductionmentioning

confidence: 99%

Frequency-Domain Bounds for Nonnegative, Unsharply Band-Limited Functions

Janssen

1994

J Fourier Anal Appl

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In this paper we present a technique for proving bounds of the Boas-Kac-Lukosz type for unsharply restricted functions with nonnegative Fourier transforms. Hence we consider functions F .x/ ≥ 0, the Fourier transform f .u/ of which satisfies |f .u/| ≤ " for all u in a subset of .−∞; −1] ∪ [1; ∞/, and are interested in bounds on |f .u/| for |u| ≤ 1. This technique gives rise to several "epsilonized" versions of the Boas-Kac-Lukosz bound (which deals with the case f .u/ = 0, |u| ≥ 1). For instance, we find that |f .u/| ≤ L.u/ + O." 2=3 /, where L.u/ is the Boas-Kac-Lukosz bound, and show by means of an example that this version is the sharpest possible with respect to its behaviour as a function of " as " ↓ 0. The technique also turns out to be sufficiently powerful to yield the best bound as " ↓ 0 in various other cases with less severe restrictions on f .

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Comparison of continuous and discrete mixed-integrator processors

Cited by 34 publications

References 8 publications

Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

Radar Image Interpretability Analysis.

Frequency-Domain Bounds for Nonnegative, Unsharply Band-Limited Functions

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