Feature extraction of hyperspectral images based on reformulate computation of between class scatter matrixes

Author

Fard, Tayeb Alipour ; Mojaradi, Barat ; Esmaeily, Ali

Author_Institution

Dept. of Remote Sensing Eng., Kerman Grad. Univ. of Technol., Kerman, Iran

fYear

2012

fDate

22-27 July 2012

Firstpage

4126

Lastpage

4129

Abstract

This paper proposes a method called Adjusted Linear Discriminant Analysis (ALDA) for feature extraction of hyperspectral remote sensing imagery. In this method the variances of the classes are considered as a weight to estimate between-class scattering matrix appropriately. Experimental results on well known hyperspectral dataset demonstrate that compared to conventional LDA based feature extraction algorithms the overall accuracy of the classification increased.

Keywords

S-matrix theory; feature extraction; geophysical image processing; image classification; remote sensing; ALDA; adjusted linear discriminant analysis; between-class scattering matrix; classification accuracy; feature extraction; hyperspectral dataset; hyperspectral remote sensing imagery; Accuracy; Educational institutions; Feature extraction; Hyperspectral imaging; Linear discriminant analysis; Classification; Feature Extraction; Hyperspectral Imagery; Linear Discriminant Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Conference_Location

Munich

ISSN

2153-6996

Print_ISBN

978-1-4673-1160-1

Electronic_ISBN

2153-6996

Type

conf

DOI

10.1109/IGARSS.2012.6351704

Filename

6351704