The measurement and accurate visualization of the value and spatial distribution of uncertainty in remotely sensed image make up one of the key problems in the field of remote sensing. In the traditional fashions, e.g., in error matrix, the measurements based on the training data are regarded as the measures of the overall accuracy of classification models. Nevertheless, we need to estimate their performance on “out-of-sample-data” - data that have not been used in constructing the models. In this paper, the authors propose a strategy for calculating and visualizing attribute uncertainty of the classified remotely sensed imagery. With the information theory and the rough sets theory, three types of indices for measuring the attribute uncertainty of remotely sensed imagery based on pixel, object and image have been proposed. These measurements could measure effectively the attribute uncertainty and trace error as well as the propagation of uncertainty in classified remotely sensed data. In addition, corresponding visualizing fashions in different types of measurements are described.