@inproceedings{9f21758d59704a21910c789ba9abe186,
title = "Hyperspectral target detection using semi- and non- parametric methods",
abstract = "In this paper we propose novel semi- and non- parametric detectors to be used with the additive target signal model within the general detection framework of the likelihood ratio test. In the semi-parametric detector, the Gaussian mixture model is employed to estimate a lower dimensional approximation of the background probability density function (PDF), whereas a multivariate kernel density estimator is employed to estimate the PDF in the multidimensional space within the non-parametric approach. Target detection experiments are carried out using the hyperspectral airborne “Viareggio 2013 trial” data set. The detectors are shown to provide promising results for the detection of the targets of interest deployed in the scene and outperform the well-known Adaptive Matched Filter detector.",
keywords = "Hyperspectral, Non-parametric density estimation, Semi-parametric density estimation, Target detection",
author = "Assaf Dvora and Stefania Matteoli and Stanley Rotman and Gil Tidhar and Marco Diani and Mayer Aladjem",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/IGARSS.2018.8519117",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "2857--2860",
booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",
address = "United States",
}