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Improved Hyperspectral Image Testing Using Synthetic Imagery and Factorial Designed Experiments - Tapa blanda

Bellucci, Joseph P.

 
9781288395408: Improved Hyperspectral Image Testing Using Synthetic Imagery and Factorial Designed Experiments
Tapa blanda
ISBN 10: 128839540X ISBN 13: 9781288395408
Editorial: BiblioScholar, 2012

Sinopsis

The goal of any remote sensing system is to gather data about the geography it is imaging. In order to gain knowledge of the earth's landscape, post-processing algorithms are developed to extract information from the collected data. The algorithms can be intended to classify the various ground covers in a scene, identify specific targets of interest, or detect anomalies in an image. After the design of an algorithm comes the difficult task of testing and evaluating its performance. Traditionally, algorithms are tested using sets of extensively ground truthed test images. However, the lack of well characterized test data sets and the significant cost and time issues associated with assembling the data sets contribute to the limitations to this approach. This thesis uses a synthetic image generation model in cooperation with a factorial designed experiment to create a family of images with which to rigorously test the performance of hyperspectral algorithms.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

The goal of any remote sensing system is to gather data about the geography it is imaging. In order to gain knowledge of the earth's landscape, post-processing algorithms are developed to extract information from the collected data. The algorithms can be intended to classify the various ground covers in a scene, identify specific targets of interest, or detect anomalies in an image. After the design of an algorithm comes the difficult task of testing and evaluating its performance. Traditionally, algorithms are tested using sets of extensively ground truthed test images. However, the lack of well characterized test data sets and the significant cost and time issues associated with assembling the data sets contribute to the limitations to this approach. This thesis uses a synthetic image generation model in cooperation with a factorial designed experiment to create a family of images with which to rigorously test the performance of hyperspectral algorithms.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
BiblioScholar
Año de publicación
2012
Idioma
Inglés
ISBN 10 
128839540X
ISBN 13 
9781288395408
Encuadernación
Tapa blanda
Número de páginas
118
Contacto del fabricante
no disponible
Persona responsable
no disponible