Selection of feature variables in spatial discrimination of remotely-sensed satellite imagery

Author

Nishii, Ryuei

Author_Institution

Fac. of Integrated Arts & Sci., Hiroshima Univ., Japan

Volume

3

fYear

1999

fDate

1999

Firstpage

1813

Abstract

Statistical discriminant procedures based on multi-spectral images are widely used for land-cover classification. However, all images available for discrimination are not always useful for discrimination. The author considers a spatially-correlated multivariate normal distribution for the multispectral data. Under the local continuity of land-cover categories, they propose a statistical techniques for finding the best and parsimonious subset of the multispectral feature variables

Keywords

feature extraction; geophysical signal processing; geophysical techniques; image classification; multidimensional signal processing; remote sensing; terrain mapping; feature extraction; feature variables selection; geophysical measurement technique; image classification; image processing; land surface; land-cover classification; local continuity; multi-spectral image; multispectral image; remote sensing; satellite imagery; spatial discrimination; spatially-correlated multivariate normal distribution; statistical discriminant procedure; statistical discrimination; terrain mapping; Art; Covariance matrix; Gaussian distribution; Input variables; Multispectral imaging; Pixel; Remote sensing; Satellites; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International

Conference_Location

Hamburg

Print_ISBN

0-7803-5207-6

Type

conf

DOI

10.1109/IGARSS.1999.772104

Filename

772104