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Adaptive target detection in hyperspectral imaging from two sets of training samples with different means
- Source :
- Signal Processing, Signal Processing, Elsevier, 2021, 181, pp.107909-. ⟨10.1016/j.sigpro.2020.107909⟩, Signal processing, 181 (2021). doi:10.1016/j.sigpro.2020.107909, info:cnr-pdr/source/autori:Besson O.; Vincent F.; Matteoli S./titolo:Adaptive target detection in hyperspectral imaging from two sets of training samples with different means/doi:10.1016%2Fj.sigpro.2020.107909/rivista:Signal processing (Print)/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume:181
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- 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.
- Subjects :
- Generalized likelihood ratio test
Hyperspectral imaging
Gaussian
02 engineering and technology
Constant false alarm rate
Set (abstract data type)
symbols.namesake
adaptive algortihms
remote sensing
[SPI]Engineering Sciences [physics]
0202 electrical engineering, electronic engineering, information engineering
Traitement du signal et de l'image
Electrical and Electronic Engineering
Mathematics
Spectral signature
Pixel
business.industry
target detection
020206 networking & telecommunications
Pattern recognition
Covariance
Detection
hyperspectral
Control and Systems Engineering
Likelihood-ratio test
Computer Science::Computer Vision and Pattern Recognition
Signal Processing
symbols
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
Software
Student distribution
Subjects
Details
- Language :
- English
- ISSN :
- 01651684 and 18727557
- Database :
- OpenAIRE
- Journal :
- Signal Processing, Signal Processing, Elsevier, 2021, 181, pp.107909-. ⟨10.1016/j.sigpro.2020.107909⟩, Signal processing, 181 (2021). doi:10.1016/j.sigpro.2020.107909, info:cnr-pdr/source/autori:Besson O.; Vincent F.; Matteoli S./titolo:Adaptive target detection in hyperspectral imaging from two sets of training samples with different means/doi:10.1016%2Fj.sigpro.2020.107909/rivista:Signal processing (Print)/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume:181
- Accession number :
- edsair.doi.dedup.....b0d1bf805c3ed6198e5c681f20a47b6f
- Full Text :
- https://doi.org/10.1016/j.sigpro.2020.107909⟩