This paper extends the ground-level visual attributes to high resolution remote sensing imagery to demonstrate the useful-ness of visual attributes for remote sensing tasks such as image classification. Visual attributes have been introduced as the semantic properties that transcend the categories. We train predictors from the largest ground-level attributes datasets, SUN, for 102 visual attributes, which is well defined in SUN. We first form an attribute-based representation for the remote sensing imagery with the output of trained attribute predictors. We then evaluate the classification performances of the attribute-based representation against traditional features. Extensive experiments on the ground-level baseline dataset scene 15 and remote sensing dataset UCMLU shows that ground-level visual attributes outperform the traditional low-level features in the classification problem, and the combination of ground-level visual attribute and low-level features obtains best classification rate. Moreover, we demonstrate that attribute-based representation is much more semantically powerful than the low-level features.