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Texture-based classification of remotely sensed images
by A. Suruliandi; S. Jenicka
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 8, No. 4, 2015


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.

Online publication date: Fri, 10-Jul-2015


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