This work presents a novel target detector that combines a nonparametric approach for conditional probability density function (pdf) estimation and an adaptive estimation of the target strength of the additive model it is based on. The variable bandwidth kernel density estimator is employed for pdf estimation within the Generalized Likelihood Ratio Test (GLRT) framework and a closed-form solution is found. Experimental results featuring hyperspectral data of a real subpixel target detection scenario reveal the potential of the proposed approach.

Nonparametric Target Detection with Target Strength Estimation for Hyperspectral Images

Matteoli S.;DIani M.;Corsini G.
2019-01-01

Abstract

This work presents a novel target detector that combines a nonparametric approach for conditional probability density function (pdf) estimation and an adaptive estimation of the target strength of the additive model it is based on. The variable bandwidth kernel density estimator is employed for pdf estimation within the Generalized Likelihood Ratio Test (GLRT) framework and a closed-form solution is found. Experimental results featuring hyperspectral data of a real subpixel target detection scenario reveal the potential of the proposed approach.
2019
978-1-5386-9154-0
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1031709
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact