Optimal polarimetric processing for enhanced target detection

Novak, Leslie M.; Burl, Michael C.; Irving, W.W.; Owirka, G.J.

doi:10.1109/ntc.1991.147989

Cited by 56 publications

(70 citation statements)

References 7 publications

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“…The approach includes four stages: rough object detection, feature extraction from the potential object regions, feature selection based on the training data and the final discrimination. The first stage is based on the Lincoln Lab ATR system and the second stage uses some features (first 10 of the 20 features) used in their system [19,34,35]. In the feature selection stage, GA is used to select a best feature subset, defined as a particular set of features, which is the best in discriminating the object from the natural clutter false alarm.…”

Section: Technical Approachmentioning

confidence: 99%

Automatic Design and Synthesis of Automatic Target Recognition (ATR) Systems Using Learning Paradigms

Bhanu¹,

Lin²,

Krawiec³

2003

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Section: Technical Approachmentioning

confidence: 99%

Automatic Design and Synthesis of Automatic Target Recognition (ATR) Systems Using Learning Paradigms

Bhanu¹,

Lin²,

Krawiec³

2003

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“…In some situations it may be desirable to approximate F(x) by some function such as a polynomial. For the problem of interest, the desired functional replacement is Z + -ln(l-F (2)) (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23) F'inally, it should be observed that if the pixel intensity Z . is replaced by any ZMNL monotonic function of Z, the above procedure still yields a random variable whose probability density is the same negative exponential.…”

Section: Transformation Of Probability Distributionsmentioning

confidence: 99%

“…In particular, Z can be replaced by Z1/2, which is the modulus of the complex pixel value. The choice of whether to use Z or some other monotonic function of Z prior to the replacement of (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23) is simply a matter o f computational convenience. 6.…”

Section: Transformation Of Probability Distributionsmentioning

confidence: 99%

The detection of weak signal patterns in radar ocean intensity images

Manasse¹

1996

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The detection of weak patterns in radar ocean RCS images is complicated by the fact that signals and noise are interactive rather.than additive, and the ambient noise background i s non Gaussian, or even strongly non Gaussian at low grazing angles. This paper addresses this extremely difficult problem with the aid of two simplifying assumptions, 1 ) the signal modulation i s weak and 2) the departure from Gaussianity is small. In situations where the departure from Gaussianity is large, an approach is suggested for reducing this non Gaussianity. The relevant weak signal detection theory, based on the Likelihood ratio, is reviewed and adapted for use in the analysis. The approach to this problem, similar to that previously used by the author for complex images, is facilitated by approximating the multivariate probability distributions as a composite integral involving underlying processes which are assumed to be Gaussian. This formulation, subject to the approximations in the analysis, permits the derivation of an ideal detection statistic (which determines the form of the optimum receiver) and a SNR which characterizes detection performance in the weak signal limit. Some implications for image processing are discussed and directions for future analysis are suggested

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“…With PolSAR data containing more available information, e.g., amplitude and relative phase, than single-polarization SAR data, the scattering behavior of the ship target and sea clutter can be analyzed in detail. The methods of the classical polarimetric whitening filter (PWF) [9], polarimetric cross-entropy (PCE) [10], reflection symmetry analysis (RS) [11] and polarimetric notch filter (PNF) [12] can effectively reveal the difference in scattering behavior between the ship target and sea clutter and are widely utilized to separate ship targets from the surrounding sea clutter.…”

Section: Introductionmentioning

confidence: 99%

Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

Lang

Tao

et al. 2017

Remote Sensing

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Scattering mechanism (SM) analysis is a promising technique for ship detection and classification in polarimetric SAR (PolSAR) images. In this paper, a four-component model-based decomposition method incorporating surface, double-bounce, volume and cross-polarized components is proposed for analyzing the SMs of ships. A novel cross-polarized scattering component capable of discriminating between the HV scattering power generated by the oriented scatterers on ships from that by volume scatterers is proposed as a means to address the problem of volume scattering power overestimation. In the decomposition stage, by taking into account both the real and imaginary parts of the elements [T] HV (1, 3) and [T] HV (2, 3) of the observed coherency matrix, the proposed cross-polarized component can preserve the reflection asymmetry information completely, which is an essential property of man-made targets, such as ships. Based on the proposed decomposition method and an analysis of the different SMs between ships and sea clutter, a novel ship detection metric defined as M = lnis proposed. Experimental results conducted on RadarSat-2 quad-polarimetric data validate the proposed four-component decomposition method as being more suitable for analyzing the SMs of ship targets than the existing helix matrix-based decomposition methods. Additionally, we find that the proposed ship detection metric can effectively enhance the signal-to-clutter ratio (SCR) and improve ship detection performance.

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Optimal polarimetric processing for enhanced target detection

Cited by 56 publications

References 7 publications

Automatic Design and Synthesis of Automatic Target Recognition (ATR) Systems Using Learning Paradigms

Automatic Design and Synthesis of Automatic Target Recognition (ATR) Systems Using Learning Paradigms

The detection of weak signal patterns in radar ocean intensity images

Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

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