Automatic Planimetric Feature Extraction, based on Model-Based Image Analysis (MBIA) from Simulated Images

The goal of model-based approach is to optimize a utility function, which can be described as cost- or risk-weighted likelihood for a collection of objects and their parameters. The utility function consists, for each iteration of geometric hypotheses generation, of the matrix over the crossing of all evidence vectors and all hypotheses vectors. This confusion matrix is multiplied with a benefit/cost matrix. Then study of the utility (parameter) function gives the overall quality in the minimum value of utility. Knowledge about spectral properties of objects is already handled through the Bayesian inversion of the probability for spectral value given class. Knowledge about the geometry of objects can be obtained directly from the data through likelihood-based segmentation and/or from an existing geographic object model. The efficiency of the model-based approach is compared with maximum likelihood classification and fuzzy classification and it has promising results.

کلیدواژه ها:

Model-based ، Hypothesis vectors ، Evidence vectors ، Confusion matrix ، Benefit/Cost matrix ، Maximum likelihood classification ، Fuzzy classification