A prototype expert system for interpretation of remote sensing image data

Automated image interpretation systems of remotely sensed images are of great help in the present scenario of growing applications. In this paper, we have critically studied visual interpretation processes for urban land cover and land use information. It is observed that the core activity of interpretation can be described as plausible combinations of pieces of evidential information from various sources such as images, collateral data, experiential knowledge and pragmatics. Interpretation keys for the interpretation of standard false colour composites are considered to be tone/colour, pattern, texture, size, shape, association, relief and season. These interpretation keys encompass the spectral, spatial and temporal knowledge required for image interpretation. Our focus is on a knowledge-based approach for interpretation of standard false colour composites (fcc). Basic information required for a knowledge-based approach is of four types viz., spectral, spatial, temporal and heuristic. Generic classes and subclasses of image objects are identified for the land use/land cover theme. Logical image objects are conceptualised as region/area, line and point objects. An object-oriented approach for the representation of spectral and spatial knowledge has been adopted. Heuristic information is stored in rules. The Dempster-Shafer theory of evidence is used to combine evidence from various interpretation keys for identification of generic class and subclass of a logical image object. Analysis of some Indian Remote Sensing Satellite images has been done using various basic probability assignments in combination with learning. Explanation facility is provided by tracing the rules fired in the sequence