Which method is used to assign categories to raster cells to produce discrete classes?

Raster classification is the method used to assign categories to raster cells in order to produce discrete classes. This process involves the classification of pixel values based on their characteristics into distinct groups or classes, such as land cover types (e.g., forest, water, urban), which represent specific categories of data.

In raster classification, various algorithms can be applied, including supervised and unsupervised classification techniques. Supervised classification requires prior knowledge of the classes and uses training samples to develop a model, while unsupervised classification groups the cells based on natural groupings in the data without prior knowledge of classes.

The correct choice highlights an essential technique within GIS, as it helps in managing, analyzing, and visualizing spatial data effectively. It allows for more accessible interpretation and utilization of information represented in raster format, typically aiding in decision-making processes related to environmental management, urban planning, and resource management.

The other methods listed do not pertain to the discrete classification of raster cells: spatial interpolation is about estimating values in areas where data is not available, vectorization refers to converting raster data into vector format, and geocoding involves linking data to geographic coordinates, which does not assign category classifications to raster cells.