8.5.2. Posterior Probability Estimation
In the above section, it has been explained that in order to determine the necessity and sufficiency measures N and S. The posterior probabilities such as P(eh) and P(e ) are provided by domain experts. Sometimes, the system engineer may have to participate in the process of determining P(eh) and P(e ) as will be explained in later part of this lecture (e.g., classification of land-use/cover types from remotely sensed images).
In spatial handling, domain experts may provide us the spatial data required or we are requested to collect further data from sources such as remote sensing images. Domain experts may also provide us their knowledge on where a specific hypothesis has been validated. It might be our responsibility to transform this type of knowledge into a computer system. The processes of collecting and encoding of expert knowledge is called knowledge acquisition and knowledge representation, respectively. While various complex computer structures for knowledge representation may be used, relatively simple procedures such as use of parametric statistical models or non-parametric look-up tables are often used. For the parametric method, a further readings is Richards (1986). For the non-parametric approach, refer to Duda and Hart (1973). Remote sensing image classification can be considered as a process of hypothesis test in which remotely sensed data are treated as evidences and a number of classes represent a list of hypotheses. In remote sensing image classification the equivalent of processes of knowledge acquisition and representation is supervised training (Gong and Howarth, 1990; and Gong and Dunlop, 1991).