Supervised Classification Techniques
Supervised classification is the technique most often used for the quantitative analysis of remote sensing image data. At its core is the concept of segmenting the spectral domain into regions that can be associated with the ground cover classes of interest to a particular application. In practice those regions may sometimes overlap. A variety of algorithms is available for the task, and it is the purpose of this chapter to cover those most commonly encountered. Essentially, the different methods vary in the way they identify and describe the regions in spectral space. Some seek a simple geometric segmentation while others adopt statistical models with which to associate spectral measurements and the classes of interest. Some can handle user-defined classes that overlap each other spatially and are referred to as soft classification methods; others generate firm boundaries between classes and are called hard classification methods, in the sense of establishing boundaries rather than having anything to do with difficulty in their use. Often the data from a set of sensors is available to help in the analysis task. Classification methods suited to multi-sensor or multi-source analysis are the subject of Chap. 12.