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The main assumptions made in the study were the selected training pixels to be pure elements of their representative land cover classes, the fine resolution pixels
SRM techniques provide land cover information in a finer spatial resolution using the outputs of soft classifiers. The outputs of soft classifiers used in SRM
Frohn (2006) employed the square pixel metric (SqP) to distinguish image objects between land-cover categories that had similar spectral properties but different shape
Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing
The manual methods of interpretation of satellite images and classification of land cover are generally performed based on the observation using the data acquired
Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes
All the four multiband images were classified using support vector machine (SVM) classifier into four most abundant land cover classes, viz, hard surface, vegetation, water
The hyperspectral profile for seven major urban land cover classes was generated to analyze the class separability and selection of bands that are best suitable
