Comparative analysis of hyperspectral dimension reduction methods

Author

Kozal, A.O. ; Teke, Mustafa ; Ilgin, H.A.

Author_Institution

TUBITAK UZAY (Turkiye Bilimsel ve Teknolojik Arastirma Kurumu, Uzay Teknolojileri Arastirma Enstitusu), ODTU Yerleskesi, Ankara, Turkey

fYear

2013

fDate

24-26 April 2013

Firstpage

1

Lastpage

4

Abstract

Hyperspectral sensors generate images in narrow bands in continuous manner with hundreds of spectral bands. The data with large number of bands require more processing power to classify. To decrease the redundancy in hyperspectral images and increase classifying efficiency with less number of bands, dimension reduction techniques are applied. In this paper, linear and non-linear dimension reduction methods are compared in classification performance and calculation time.

Keywords

hyperspectral imaging; image classification; remote sensing; dimension reduction techniques; hyperspectral dimension reduction methods; hyperspectral images; hyperspectral sensors; narrow band image generation; nonlinear dimension reduction methods; spectral bands; Hyperspectral imaging; Image classification; Information processing; Measurement; Presses; Principal component analysis; dimension reduction; hyperspectral image classification; hyperspectral imaging; remote sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2013 21st

Conference_Location

Haspolat

Print_ISBN

978-1-4673-5562-9

Electronic_ISBN

978-1-4673-5561-2

Type

conf

DOI

10.1109/SIU.2013.6531487

Filename

6531487