Asymptotically Optimum Quadratic Detection in the Case of Subpixel Targets

Golikov, Victor; Lebedeva, Olga; Moreno, Andres Castillejos; Ponomaryov, Volodymyr

doi:10.1587/transfun.e94.a.1786

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…Note that the detector SpMD [Eq. (13)] uses only changes in target energy, and the detector 2 uses changes in target energy and changes in background energy but does not use change in the shape of the background spectrum under hypothesis H 1 .…”

Section: Glrt In the Case Of Unknown Covariance Matrix R Sbnmentioning

confidence: 99%

“…(19)], the well-known SpMD 15 [Eq. (13)], and the recently proposed HMSD [Eq. (15)] in the case of the difference between statistical parameters (the means, the variances, and covariances) of the unstructured background and objects.…”

Section: Performance Assessmentmentioning

confidence: 99%

“…The common detection algorithms include the spectral feature fitting (SFF), [1][2][3][4] orthogonal subspace projection (OSP), [5][6][7] constrained energy minimization (CEM), 5,8 matched filter (MF), 1,[9][10][11][12][13][14][15][16][17] and mixture tuned matched filtering (MTMF). [18][19][20][21][22] The SFF method uses a spectral absorption feature in its comparisons.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. 13, the asymptotic optimum quadratic detector of the subpixel targets in a Gaussian environment has been designed. It was assumed that the background covariance matrix and power under the null hypothesis are a priori known, but under the alternative hypothesis is that the background power can be a priori unknown.…”

Section: Introductionmentioning

confidence: 99%

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Generalized likelihood ratio test for optical subpixel objects’ detection with hypothesis-dependent background covariance matrix

Golikov

Samovarov

Zhilyakov

et al. 2020

J. Appl. Rem. Sens.

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Much interest has arisen in the problem of detecting weak optical subpixel objects in a sequence of images immersed in a heavy homogeneous Gaussian clutter background. In optical systems, the presence of the objects changes the background plus the channel noise covariance matrix. Hence, this matrix may be different under null and alternative hypotheses. Because the maximum likelihood estimate of the background covariance matrix is not available when using the primary data set, we use an approximate estimate of a certain number of eigenvalues of the background covariance matrix that are available for estimation. We derived the general likelihood ratio test for the problem at hand and obtained the modified spectrum matching detector (MSpMD). Using the simulation results, we contrast it with well-known spectrum matching detectors and the hypothesis-independent matched subspace detectors. We also present experimental results of small subpixel objects detection on an agitated sea surface, using digital optical experiments that demonstrate the validation of the simulating results. We show the advantage of the MSpMD that allows to detect small subpixel floating objects on an agitated sea surface even with the same means and variances of the reflected signals from the sea surface and the object. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Glrt In the Case Of Unknown Covariance Matrix R Sbnmentioning

confidence: 99%

Section: Performance Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generalized likelihood ratio test for optical subpixel objects’ detection with hypothesis-dependent background covariance matrix

Golikov

Samovarov

Zhilyakov

et al. 2020

J. Appl. Rem. Sens.

Self Cite

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show abstract

“…In the literature, the detection problem with hypothesis-dependent noise power has been first considered in [13] for the case of the white normal noise. In [14], the MMSD of the subpixel targets in a Gaussian environment has been designed. The MMSD modifies the classical matched subspace detector (MSD) by adding to it a term proportional to the square of the background power variation between and .…”

Section: Introductionmentioning

confidence: 99%

Adaptive Detection of Subpixel Targets With Hypothesis Dependent Background Power

Golikov

Lebedeva

2013

IEEE Signal Process. Lett.

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We design and assess an adaptive scheme to detect a subpixel target in a sequence of images in the presence of an additive correlated Gaussian background. The presence of the subpixel target decreases the background power that hence may be different under the null and alternative hypotheses. We use the generalized likelihood ratio test (GLRT) to adapt the recently proposed modified matched subspace detector (MMSD) to unknown background variances under the null and alternative hypotheses using the secondary and primary data, respectively. We derive a modified adaptive subspace detector (MASD) that is sensitive to both energy in the target subspace and reduced energy in the orthogonal subspace. We contrast it with the MMSD and the well-known adaptive cosine estimator (ACE). Numerical simulations attest to the validity of the theoretical analysis and show that the proposed detector performance outperforms the ACE, especially in the case of dark subpixel targets. The performance-degrading effects of limited secondary data are presented for the proposed detector.Index Terms-Adaptive detector, hypothesis dependent background power, subpixel subspace detection.

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GLRT subspace detection of multi-pixel targets with known and unknown spatial parameters in presence of signal-dependent background power

Golikov

Lebedeva

Blanco

2016

Computers & Electrical Engineering

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Asymptotically Optimum Quadratic Detection in the Case of Subpixel Targets

Cited by 8 publications

References 13 publications

Generalized likelihood ratio test for optical subpixel objects’ detection with hypothesis-dependent background covariance matrix

Generalized likelihood ratio test for optical subpixel objects’ detection with hypothesis-dependent background covariance matrix

Adaptive Detection of Subpixel Targets With Hypothesis Dependent Background Power

GLRT subspace detection of multi-pixel targets with known and unknown spatial parameters in presence of signal-dependent background power

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