Sampling is needed to limit the observations for statistical analysis. In raster image analysis, various sampling schemes have been proposed for selecting pixels to test. Choices to be made relate to the design of the sampling strategy, the number of samples required, and the area of the samples. Recommended sampling strategies in the context of land cover data are simple random sampling or stratified random sampling. The number of samples may be related to two factors in accuracy assessment: (1) the number of samples that must be taken in order to reject a data set as being inaccurate; or (2) the number of samples required to determine the true accuracy, within some error bounds, of a data set. Sampling theory is used to determine the number of samples required. The number of samples must be traded-off against the area covered by a sample unit. A sample unit can be a point but it could also be an area of some size; it can be a single raster element but may also include surrounding raster elements. Among other considerations, the “optimal” sample-area size depends on the heterogeneity of the class.