Int. J. of Signal and Imaging Systems Engineering   »   2015 Vol.8, No.4

 

 

Title: Texture-based classification of remotely sensed images

 

Authors: A. Suruliandi; S. Jenicka

 

Addresses:
Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli 627012, Tamil Nadu, India
Einstein College of Engineering, Tirunelveli 627007, Tamil Nadu, India

 

Abstract: Texture is a significant spatial property that precisely captures patterns in a satellite image. Texture-based classification plays a vital role in land use-land cover application of remotely sensed images. In this paper, texture features were extracted using Multivariate Local Binary Pattern (MLBP), Multivariate Local Texture Pattern (MLTP), Multivariate Advanced Local Binary Pattern (MALBP), wavelet and Gabor wavelet. Texture-based classification was performed on IRS-P6, LISS-IV data, and the results were evaluated based on error matrix, classification accuracy and kappa statistics. From the experiments, it was found that MLTP outperformed other texture models.

 

Keywords: texture classification; feature extraction; Gabor wavelet; signal processing; remote sensing; error matrix; classification accuracy; kappa statistics; local binary pattern; LBP; local texture pattern; LTP; texture models; modelling; satellite images.

 

DOI: 10.1504/IJSISE.2015.070546

 

Int. J. of Signal and Imaging Systems Engineering, 2015 Vol.8, No.4, pp.260 - 272

 

Available online: 10 Jul 2015

 

 

Editors Full text accessPurchase this articleComment on this article