1. Adaptive target detection in hyperspectral imaging from two sets of training samples with different means
- Author
-
François Vincent, Olivier Besson, Stefania Matteoli, Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Institute of Electronics, Computer and Telecommunication Engineering [Milano] (IEIIT-CNR ), Consiglio Nazionale delle Ricerche [Torino] (CNR), Consiglio Nazionale delle Ricerche - CNR (ITALY), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), and Département d'Electronique, Optronique et Signal - DEOS (Toulouse, France)
- 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 - 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.
- Published
- 2021
- Full Text
- View/download PDF