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The purpose of this research work is to compare hyperspectral and multispectral imagery to discriminating land-cover classes by k-nearest neighbor algorithm (KNN) supervised classification with migrating means clustering unsupervised classification (MMC) method and in last develop spectral library. We used Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes’ separation.

Land cover classification and build spectral library from hyperspectral and multi-spectral satellite data: A data comparison study in Samara, Russia