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Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance

Name: Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance
Brand: Biblioscholar
SKU: 08239730
Price: 57.37 EUR
Availability: InStock
Author: Matthew T Davis
ISBN: 9781288229970

Matthew T Davis

Language

English

Book Paperback

Libristo code: 08239730

Publishers Biblioscholar, October 2012

Detecting and identifying objects of interest is the goal of all remote sensing. New advances, speci... Full description

Libristo code: 08239730

140 b

57.37 €

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Detecting and identifying objects of interest is the goal of all remote sensing. New advances, specifically in hyperspectral imaging technology have provided the analyst with immense amounts of data requiring evaluation. Several filtering techniques or anomaly detection algorithms have been proposed. However, most new algorithms are insuciently verified to be robust to the broad range of hyperspectral data being made available. One such algorithm, AutoGAD, is tested here via two separate robust parameter design techniques to determine optimal parameters for consistent performance on a range of data with large attribute variances. Additionally, the results of the two techniques are compared for overall e ectiveness. The results of the test as well as optimal parameters for AutoGAD are presented and future research e orts proposed.

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About the book

Full name Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance

Author Matthew T Davis

Language

English

Binding Book - Paperback

Date of issue 2012

Number of pages 160

EAN 9781288229970

ISBN 9781288229970

Libristo code 08239730

Publishers Biblioscholar

Weight 299

Dimensions 189 x 246 x 9

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