Sub-pixel detection in hyperspectral imaging with elliptically contoured t-distributed background

Besson, Olivier; Vincent, François

doi:10.1016/j.sigpro.2020.107662

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Again, we see that the popular Kelly's detector has to be slightly corrected by a different factor, namely n n+1 in place of 1, when considering non-zero mean data. Moreover, as already noticed in [28], this expression is the same for both Gaussian and Student distributed background, giving it an optimality for a broader class of distributions than initially expected.…”

Section: One-step Glrtsupporting

confidence: 67%

Adaptive target detection in hyperspectral imaging from two sets of training samples with different means

2021

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In this paper, we consider local detection of a target in hyperspectral imaging and we assume that the spectral signature of interest is buried in a background which follows an elliptically contoured distribution with unknown parameters. In order to infer the background parameters, two sets of training samples are available: one set, taken from pixels close to the pixel under test, shares the same mean and covariance while a second set of farther pixels shares the same covariance but has a different mean. When the whole data samples (pixel under test and training samples) follow a matrix-variate t distribution, the one-step generalized likelihood ratio test (GLRT) is derived in closed-form. It is shown that this GLRT coincides with that obtained under a Gaussian assumption and that it guarantees a constant false alarm rate. We also present a two-step GLRT where the mean and covariance of the background are estimated from the training samples only and then plugged in the GLRT based on the pixel under test only.

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Section: One-step Glrtsupporting

confidence: 67%

Adaptive target detection in hyperspectral imaging from two sets of training samples with different means

2021

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“…In this benchmarking, we compare both SFMF and the GLRT based on the exact solution for α ( Eq. ( 5) ) with AMF [18] , FTMF [19] , obviously, but also with Kelly [41][42][43] , ACE [44][45][46] , the modified FTMF from [47] and SPADE [47] . We use the so-called false alarm scores to compare these algorithms, namely the number of pixels having their detector's output strictly higher than the one for the target pixel.…”

Section: Performance Evaluationmentioning

confidence: 99%

Target detection in hyperspectral imaging combining replacement and additive models

Vincent

Besson

2021

Signal Processing

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“…( 1) , being an approximation when the target size is small with respect to the pixel's area (η << 1) . Detection schemes based on this more representative model have proven their superiority in the target detection context [21][22][23][24] . But, it seems more difficult to use it in the present case of AD, as the number of unknowns would be bigger than the size of the data.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection for replacement model in hyperspectral imaging

2021

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In this paper we consider Anomaly Detection in the hyperspectral context, and we extend the popular RX detector, initially designed under the standard additive model, to the replacement model case. Indeed, in this more realistic framework, the target, if present, is supposed to replace a part of the background. We show how to estimate this background power variation to improve the standard RX scheme. The obtained Replacement RX (RRX) is shown to be closed-form and outperforms the standard RX on a real data benchmark experiment.

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Sub-pixel detection in hyperspectral imaging with elliptically contoured t-distributed background

Cited by 8 publications

References 18 publications

Adaptive target detection in hyperspectral imaging from two sets of training samples with different means

Adaptive target detection in hyperspectral imaging from two sets of training samples with different means

Target detection in hyperspectral imaging combining replacement and additive models

Anomaly detection for replacement model in hyperspectral imaging

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