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In this study, a multi-scale approach was used for classifying land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that they are located within) from coarser segmentations of the same scene, and this set of super-object information was used as additional input data for image classification. The accuracies of classifications that included super-object variables were compared with the classification accuracies of image segmentations that did not include super-object information. The highest overall accuracy and kappa coefficient achieved without super-object information was 78.11% and 0.727%, respectively. When single pixels or fine-scale image segments were assigned the statistics of their super-objects prior to classification, overall accuracy increased to 84.42% and the kappa coefficient increased to 0.804.
