Influence of Number of Features on Texture Based Residential Area Extraction from Remotely Sensed Imagery

Author

Wang, Min

Author_Institution

Coll. of Geogr. Sci., Nanjing Normal Univ., Nanjing, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

4

Abstract

In this study, we implement and compare two texture analytical methods, 2-D Gabor and GLCM, for residential area extraction from high spatial resolution remotely sensed imagery. We elaborately investigate the influence of different settings to the outputs of both methods. Experiments show that both methods have the potential for texture based residential area segmentation, while 2-D Gabor presents an overall higher precision and consistency. Increasing the number of features, however, seems to contribute little to the accuracy of both methods.

Keywords

Gabor filters; feature extraction; geophysical signal processing; image segmentation; image texture; remote sensing; 2D Gabor; grey level co-occurrence matrices; high spatial resolution remotely sensed imagery; texture analytical methods; texture based residential area extraction; Data mining; Educational institutions; Filtering; Gabor filters; Geography; Image analysis; Image segmentation; Image texture analysis; Remote sensing; Spatial resolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5304435

Filename

5304435