@inproceedings{e4f62ee8b7ec44e490abe4419dcecfa2,
title = "Hyperspectral Target Detection Using Tree-Structured Probabilistic Graphical Model and Semi-Parametric Density Estimation",
abstract = "In this paper, we propose a novel semi-parametric target detector to be used within the general detection framework of the likelihood ratio test for the additive signal model. A tree-structured probabilistic graphical model is used to obtain lower dimensional representation of the background probability density function. The overall density estimation problem, which is reduced to finding univariate and bivariate estimates, is solved using the Gaussian mixture model. Target detection experiments are carried out using the hyperspectral airborne 'Viareggio 2013 trial' data set. The detector is shown to provide promising results for the detection of the targets of interest deployed in the scene.",
keywords = "Hyperspectral, probabilistic graphical models, semi-parametric density estimation, target detection",
author = "Assaf Dvora and Stanley Rotman and Mayer Aladjem",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2018 ; Conference date: 23-09-2018 Through 26-09-2018",
year = "2018",
month = sep,
day = "1",
doi = "10.1109/WHISPERS.2018.8747201",
language = "English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2018 9th Workshop on Hyperspectral Image and Signal Processing",
address = "United States",
}