Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.
Typically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.
A stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.
Systematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.