Constrained likelihood ratios for detecting sparse signals in highly noisy 3D data

Paris, Silvia; Suleiman, Raja Fazliza Raja; Mary, David; Ferrari, A.

doi:10.1109/icassp.2013.6638399

Cited by 11 publications

(22 citation statements)

References 27 publications

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“…In the MUSE application, the PSF is modeled according to Equation (5), where the FSF and LSF factors are well known by instrument calibration. Finally, considering a single image (at λ) makes sense, as in the overall model for the cube, it will be assumed that the random noise fluctuations are independent versus λ.…”

Section: Observation Modelmentioning

confidence: 99%

“…However, this can be easily approximated by Monte-Carlo simulations, with the parameters m λ and σ λ replaced by some pre-processing estimates that are delivered with the MUSE data cube. This approach is similar to a study presented by [5]. Another hard threshold penalization term h2(u) is introduced to take into account the max-test result:…”

Section: Configuration Priormentioning

confidence: 99%

“…Some of the objects show very low SNR, and the objective is to test the performances of different algorithms on this cube. A sub-cube of 28 × 26 pixels by 3600 wavelengths of these data has already been used in [5], and our method here can be compared with the constrained likelihood ratio proposed in [5] on this sub-cube. Figure 2 presents the results obtained for 10 4 RJMCMC iterations.…”

Section: Application To Hyperspectral Datamentioning

confidence: 99%

“…The main drawback of these methods is the assumption that the noise properties are known a priori. Recent studies have proposed the detection of sparse signals in noisy hyperspectral images [5], [6]. Sparse representation is based on a dictionary of spectral components.…”

Section: Introductionmentioning

confidence: 99%

“…Sparse representation is based on a dictionary of spectral components. In [5], a constrained likelihood ratio was proposed that exploited the spread of the signal in three dimensions using the PSF. A specific dictionary that was adapted to the signal to be detected was used to decrease the false-alarm probability, and this dictionary was optimized by techniques such as kernel singular value decomposition [7] or minimax [8].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data

Meillier

Chatelain

Michel

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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To cite this version:Céline Meillier, Florent Chatelain, Olivier Michel, Hacheme Ayasso. Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. Proceedings of ICASSP 2014, pp.1905-1909, 2014 ABSTRACTIn this study, a method that aims at detecting small and faint objects in noisy hyperspectral astrophysical images is presented. The particularity of the hyperspectral images that we are interested in is the high dynamics between object intensities. Detection of the smallest and faintest objects is challenging, because their signal-to-noise ratio is low, and if the brightest objects are not well reconstructed, their residuals can be more energetic than faint objects. This paper proposes a marked point process within a nonparametric Bayesian framework for the detection of galaxies in hyperspectral data. The efficiency of the method is demonstrated on synthetic images, and it provides good results for very faint objects in quasi-real astrophysical hyperspectral data.

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Section: Observation Modelmentioning

confidence: 99%

Section: Configuration Priormentioning

confidence: 99%

Section: Application To Hyperspectral Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data

Meillier

Chatelain

Michel

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Extended faint source detection in astronomical hyperspectral images

Courbot

Mazet

Monfrini³

et al. 2017

Signal Processing

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International audienceThe circum-galactic medium consists in gas orbiting around galaxies, whose faintness prevents any complete and easy detection. A powerful tool to detect such pattern can be found in using hyperspectral imaging. Nevertheless, detection in hyperspectral datacubes faces various problems, including well-fitted signal and noise descriptions to ensure further discrimination. A specificity of astronomical images resides in dealing with faint and very noisy signals. In this paper, we introduce a new constrained generalized likelihood ratio test adapted to the problem and a compound test to exploit most of the available information. We also investigate the use of both spatial information and multiple observations on a single scene, to enhance robustness. Numerical experiments on synthetic data are performed to quantify the gain of the different approaches. Finally, results on real hyperspectral astronomical data are presented, which may map for the first time observation of the circum-galactic medium around faint and distant galaxie

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The MUSE Hubble Ultra Deep Field Survey

Bacon

Conseil

Mary

et al. 2017

A&A

349

397

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We present the MUSE Hubble Ultra Deep Survey, a mosaic of nine MUSE fields covering 90% of the entire HUDF region with a 10-h deep exposure time, plus a deeper 31-h exposure in a single 1.15 arcmin 2 field. The improved observing strategy and advanced data reduction results in datacubes with sub-arcsecond spatial resolution (0 . 65 at 7000 Å) and accurate astrometry (0 . 07 rms). We compare the broadband photometric properties of the datacubes to HST photometry, finding a good agreement in zeropoint up to m AB = 28 but with an increasing scatter for faint objects. We have investigated the noise properties and developed an empirical way to account for the impact of the correlation introduced by the 3D drizzle interpolation. The achieved 3σ emission line detection limit for a point source is 1.5 and 3.1 × 10 −19 erg s −1 cm −2 for the single ultra-deep datacube and the mosaic, respectively. We extracted 6288 sources using an optimal extraction scheme that takes the published HST source locations as prior. In parallel, we performed a blind search of emission line galaxies using an original method based on advanced test statistics and filter matching. The blind search results in 1251 emission line galaxy candidates in the mosaic and 306 in the ultradeep datacube, including 72 sources without HST counterparts (m AB > 31). In addition 88 sources missed in the HST catalog but with clear HST counterparts were identified. This data set is the deepest spectroscopic survey ever performed. In just over 100 h of integration time, it provides nearly an order of magnitude more spectroscopic redshifts compared to the data that has been accumulated on the UDF over the past decade. The depth and high quality of these datacubes enables new and detailed studies of the physical properties of the galaxy population and their environments over a large redshift range.

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Constrained likelihood ratios for detecting sparse signals in highly noisy 3D data

Cited by 11 publications

References 27 publications

Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data

Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data

Extended faint source detection in astronomical hyperspectral images

The MUSE Hubble Ultra Deep Field Survey

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