Johnson distribution models of hyperspectral image data clusters

Autor:	Steven C. Gustafson, Eduardo C. Meidunas
Rok vydání:	2006
Předmět:	Mahalanobis distance Geography Distribution (mathematics) business.industry Outlier Hyperspectral imaging Pattern recognition Artificial intelligence business Empirical distribution function Automated method Image (mathematics)
Zdroj:	SPIE Proceedings.
ISSN:	0277-786X
DOI:	10.1117/12.663965
Popis:	The Johnson System for characterizing an empirical distribution is used to model the non-normal behavior of Mahalanobis distances in material clusters extracted from hyperspectral imagery data. An automated method for determining Johnson distribution parameters is used to model Mahalanobis distance distributions and is compared to an existing method which uses mixtures of F distributions. The results lead to a method for determining outliers and mitigating their effects.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ad5b8095788a0ab0a2ab18c7f23ca083 https://doi.org/10.1117/12.663965 Zobrazit plný text záznamu