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- W1720557560 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." @default.
- W1720557560 created "2016-06-24" @default.
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- W1720557560 date "2015-01-01" @default.
- W1720557560 modified "2023-10-17" @default.
- W1720557560 title "Texture-based classification of remotely sensed images" @default.
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- W1720557560 doi "https://doi.org/10.1504/ijsise.2015.070546" @default.
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