Non-Gaussian Linear Mixing Models for Hyperspectral Images
Author
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-10-04
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Engineering Sciences and Information Technology
Information Technology and Computer Science
Abstract EN
Modeling of hyperspectral data with non-Gaussian distributions is gaining popularity in recent years.
Such modeling mostly concentrates on attempts to describe a distribution, or its tails, of all image spectra.
In this paper, we recognize that the presence of major materials in the image scene is likely to exhibit nonrandomness and only the remaining variability due to noise, or other factors, would exhibit random behavior.
Hence, we assume a linear mixing model with a structured background, and we investigate various distributional models for the error term in that model.
We propose one model based on the multivariate t-distribution and another one based on independent components following an exponential power distribution.
The former model does not perform well in the context of the two images investigated in this paper, one AVIRIS and one HyMap image.
On the other hand, the latter model works reasonably well with the AVIRIS image and very well with the HyMap image.
This paper provides the tools that researchers can use for verifying a given model to be used with a given image.
American Psychological Association (APA)
Bajorski, Peter. 2012. Non-Gaussian Linear Mixing Models for Hyperspectral Images. Journal of Electrical and Computer Engineering،Vol. 2012, no. 2012, pp.1-8.
https://search.emarefa.net/detail/BIM-500552
Modern Language Association (MLA)
Bajorski, Peter. Non-Gaussian Linear Mixing Models for Hyperspectral Images. Journal of Electrical and Computer Engineering No. 2012 (2012), pp.1-8.
https://search.emarefa.net/detail/BIM-500552
American Medical Association (AMA)
Bajorski, Peter. Non-Gaussian Linear Mixing Models for Hyperspectral Images. Journal of Electrical and Computer Engineering. 2012. Vol. 2012, no. 2012, pp.1-8.
https://search.emarefa.net/detail/BIM-500552
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-500552