This paper deals with the sub-pixel target detection problem in hyper-spectral images. The problem is approached by modeling the mixed spectrum with both the Linear Mixing Model (LMM) and the Stochastic Mixing Model (SMM). A detection strategy is derived by assuming the SMM. In the proposed algorithm, detection is accomplished by testing the values of the Maximum A-priori Probability (MAP) estimate of the target’s abundance that represent the fraction of the spectrum in the observed pixel due to the target. The algorithm has been applied to experimental images and the results have been compared with the ones obtained by the Adaptive Matched Subspace Detector (AMSD) based on the LMM.

New statistical detector for known spectral signature targets in hyper-spectral images

;CORSINI, GIOVANNI;DIANI, MARCO
2004

Abstract

This paper deals with the sub-pixel target detection problem in hyper-spectral images. The problem is approached by modeling the mixed spectrum with both the Linear Mixing Model (LMM) and the Stochastic Mixing Model (SMM). A detection strategy is derived by assuming the SMM. In the proposed algorithm, detection is accomplished by testing the values of the Maximum A-priori Probability (MAP) estimate of the target’s abundance that represent the fraction of the spectrum in the observed pixel due to the target. The algorithm has been applied to experimental images and the results have been compared with the ones obtained by the Adaptive Matched Subspace Detector (AMSD) based on the LMM.
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: http://hdl.handle.net/11568/201790
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact