PDR and LRMAP detection tests applied to massive hyperspectral data

Paris, Silvia; Mary, David; Ferrari, A.

doi:10.1109/camsap.2011.6136054

Cited by 5 publications

(15 citation statements)

References 5 publications

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“…In [20][21][22], we introduced two detection tests based on MAP estimates, which were shown to be more powerful for sparse signals than classical methods, such as the unconstrained GLR test [19]. A connection was found with earlier works of Fan [23] and Basu [24], and with the Max test described in [5].…”

Section: Introduction and Previous Workmentioning

confidence: 95%

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

Paris

Suleiman

Mary

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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We propose a method aimed at detecting weak, sparse signals in highly noisy three-dimensional (3D) data. 3D data sets usually combine two spatial directions x and y (e.g. image or video frame dimensions) with an additional direction λ (e.g. temporal, spectral or energy dimension). Such data most often suffer from information leakage caused by the acquisition system's point spread functions, which may be different and variable in the three dimensions. The proposed test is based on dedicated 3D dictionaries, and exploits both the sparsity of the data along the λ direction and the information spread in the three dimensions. Numerical results are shown in the context of astrophysical hyperspectral data, for which the proposed 3D model substantially improves over 1D detection approaches.

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Section: Introduction and Previous Workmentioning

confidence: 95%

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

Paris

Suleiman

Mary

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…To our knowledge, the problem of detecting a faint extended source in multispectral or hyperspectral images has not been previously handled: a very common aspect of source detection in HSI is the search for punctual or quasi-punctual objects [4,5,8,9]. This is mainly because most extended sources are not faint in remote sensing and are addressed in a classification context, while instruments in other domains do not provide sufficient spatial or spectral resolutions.…”

Section: Tlr-scnmentioning

confidence: 99%

“…This test is similar to the LRMPβ from [5]. So far, this test addresses spectra pair: the reference observation may be the mean spectra of the initial detection region B from Section 2.…”

Section: Extended Source Detection Testmentioning

confidence: 99%

“…We make the assumption that the reduced centered individual components logarithms ln(TLR-s) follow, under H0, a N (0, 1) distribution [5]. We assume then that it is also the case for the reduced centered compound ln(TLR-sc).…”

Section: Extended Source Detection Testmentioning

confidence: 99%

“…A first point of interest for CGM detection is the sparsityoriented tests, as only a few spectrally-grouped coefficients are far from zero. Works on this topic include sparsity-favouring [5] and sparsity-constrained [6] detection tests. Another topic is the integration of field spread function (FSF), most often with sparsity constraints [7,8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations

Courbot

Mazet

Monfrini³

et al. 2016

2016 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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PDR and LRMAP detection tests applied to massive hyperspectral data

Cited by 5 publications

References 5 publications

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

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

Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations

Extended faint source detection in astronomical hyperspectral images

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