Contributo in atti di convegno, 2022, ENG, 10.1109/IGARSS46834.2022.9883554
Matteoli, Stefania; Diani, Marco; Corsini, Giovanni
Università di Pisa; Consiglio Nazionale delle Ricerche; Italian Naval Academy
A Bayesian Likelihood Ratio Test (LRT) detector is analytically derived here for the replacement target model and using the non-parametric variable-bandwidth kernel density estimator to model the hyperspectral background. The detector is compared to the recent Generalized LRT detector, based on the same non-parametric model for the background. Experimental results obtained on two hyperspectral sub-pixel target detection scenarios reveal the great potential of the proposed detector and set the basis for future investigations.
IEEE International Geoscience and Remote Sensing Symposium, pp. 871–874, 2022
Bayes, Hyperspectral, non-parametric, replacement model, Target Detection
IEIIT – Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
ID: 490818
Year: 2022
Type: Contributo in atti di convegno
Creation: 2023-12-28 17:38:17.000
Last update: 2024-01-09 09:56:20.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1109/IGARSS46834.2022.9883554
URL: http://www.scopus.com/record/display.url?eid=2-s2.0-85140362814&origin=inward
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:490818
DOI: 10.1109/IGARSS46834.2022.9883554
Scopus: 2-s2.0-85140362814