<title>Theory of knowledge-based image analysis with applications to SAR data of agriculture</title>

Abstract

The requirements for a theory of image analysis imply predictability of RS image measurements. RS images are predicted from a combined model of objects in 3 dimensions with samples taken at short time intervals. Image analysis is the inverse of image synthesis or image prediction. Inversion of the model of image synthesis requires additional knowledge about objects, processes and sensing. The role of knowledge is mainly to constrain the search effort in a problem space of hypotheses and parameters. The method of image analysis as reported here is a hypothesis driven method, in contrast to data driven methods of image interpretation, image processing or data fusion. In a reaction to a failing search for suitable GIS theories and structures, an alternative is reported for the classical integration of 2.5 dimensional GIS and RS with data driven image processing. The required theories and structures are taken from the domain of physical modelling. Knowledge about 3 dim objects and about processes is represented in physical models which may have a probabilistic component. Given a model for sensors, the atmosphere, radiation with matter interaction, and a set of hypotheses and parameters about objects and their state, hypotheses are evaluated and parameters are estimated. Hypothesis based analysis means comparison of hypotheses in the model domain with evidence coming from the RS measurement domain or feature domain. A specific problem addressed here is that of the estimation of geometric parameters of objects in microwave images. The treatment of prior probabilities appears to be critical. The relationship between statistics of the radiometric and geometric parameter estimators was investigated and results are reported. After the introduction of basic concepts of geometric and radiometric parameter estimation, a case of agricultural landuse classification is given. The case introduces the problem of converting classical vector data to parameterised geometric decision functions.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.