| Summary: | The inherent spatial information within a satellite remotely sensed data has shown a significant contribution to the classification of an image. In this, paper, two new textural approaches, namely the Rolling Ball Transform (RBT) and Ripping membrane (RM), were examined and analysed for image classification. the classification results are presented in the following models: (I) textural information alone, (ii) combination of best textural information and spectral data, and (iii) spectral data alone. Comparison of classification results obtained from the two new textural methods with three commonly textural extraction techniques : grey level co-occurrence, local statistical transform and convolution filtering masks were also carried out. Result of this study shows that the new textural extraction methods as one of the possible methods to increase the classification accuracies of the land use classes. The best results were obtained when textural information was combined with raw data. The new textural approaches- the RBT and RM both showed the most stable textures. RM textures from range 20 was the best overall textural extraction method.
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