Building a geo-expert system integrating Remote Sensing and GIS
Mrs. P. Venkatachalam and C.V.S.S.B.R. Murty
Centre of Studies in Resources Engineering
Indian Institute of Technology, Bombay, 400076, India
Abstract
Ideally GIS and Remote Sensing should be integrated into one Geo-Expert System which can answer questions, take action and give advice in a seemingly intelligent way based on facts contained in GIS and on the procedures and data available in digital Remote Sensing system. A hierarchy of lower level expert systems can work be more specialized and contain knowledge about a specific domain, eg. Geology, soils etc. In a way, the logic rules for interpretation and classification given in the Manual of Remote Sensing can be put into expert system for specific domain of application. Using expert system shells, a domain knowledge engineer can translate the application domain knowledge of a real expert (e.g a hydrologist or a geologist) into a knowledge base so that both Remote Sensing and Geographic Information Systems are integrated optimal for solutions in specific areas. This paper deals with the development of a knowledge based system which can assist a user to find the groundwater potential of region using a segmented Remote Sensing imagery and the related data stored in a GIS. The domain experts knowledge is constructed into a rule base with backward chining control strategy. As the rule base is not exhaustive additional rules can be imbedded into it. The system is built using PC based shell.
Introduction
Human experts in any field are frequently in great demand and are therefore, usually in short supply. Whether for repairing automobiles or drilling for oils or analyzing chemicals, there are times when access to the knowledge experience and judgement of an expert in a field would be an invaluable asset. One solution to the dilemma is the expert system, an AI Computer program specially designed to represent human expertise in a particular domain (area of expertise). An expert system contains knowledge about a particular field to assist human (Experts) to provide information to people who do not have an access to an expert in the particular field. Although both expert systems and data base programs feature retrieval of stored information, the two types of programs differ
greatly. A data base programs retrieves facts that are stored, while an expert system uses reasoning to draw conclusions from stored facts, add new facts and gives the possibility to work with uncertain and missing data.
An information system is a data base system providing answers to questions about its domain. It contains facts about the field of specialization and has facilities for maintaining. Extending and correcting these facts. Geographic information systems (GIS) are systems specialized to deal with earth related data. Remote Sensing (RS) is an activity which collects earth related data and information extraction from Remote Sensing data is done either by image processing followed by human interpretation or automated pattern recognition.
Ideally GIS and RS systems should be integrated into one Geo-Expert System (GE) which can answer questions, take action and give advice in a seemingly intelligent way based on facts contained in GIS and on the procedures and data available in a digital RS system. A hierarchy of lower level expert systems can work under the supervision of a master GES. Each lower level system can be more specialized and contain knowledge about a specific domain e.g. geology, soils etc. In a way, the logic rules for interpretation and classification given in the manual of Remote Sensing (Reeves, 1975) can be put into expert system for specific domain of application. In image processing applied to Remote Sensing, large number of subroutines and hardware are available. To make an intelligent use of them an expert advisor can be prepared which can help in translating the user's problem into a strategy so that suitable subroutines can be called and optimal parameters can be set up for analyzing the data. Presently GIS packages are often based on business type data bases. The expert system approach, will allow a more flexible way to implement optimum query language, ease data base maintenance and will add the possibility of working with uncertain or partly missing data. Using expert system approach, a domain knowledge engineer can translate the application domain knowledge of real expert (Eg. a hydrologist or a geologist) into a knowledge base so that both RS and GIS can be integrated for solutions in specific areas.
To demonstrate the integration of knowledge engineering techniques for image interpretation and classification, study on soil mapping using SPOT image has been carried out (Mulder et al., 1988). Input into the system is defined by a segmented image. The system works through a hierarchy of observations, partly following a forward chaining search strategy. Using the information provided by the user, the system concludes the most likely soil class for each region in the segmented image.
In earth resources application, one of the earliest expert systems developed was PROSPECTOR (Duda et al., 1974). It contains the rule based models for different kinds of ore deposits and helps to evaluate the favourableness of a ore deposits and helps to evaluate the favourableness of a geologic district for any kind of ore. A mixed control strategy is followed and it accommodates uncertainly in both evidences and rules. GEOMYCIN (Davis & Nanninga, 1985) demonstrates the possibility of incorporating spatial knowledge for forest management. Knowledge based systems for aerial photo interpretation have been developed (Nagao & Matusuyama 1980; McKeown , 1985). The need for Artificial Intelligence Principle in the integration of remotely sensed data with GIS has been emphasized (McKeown, 1987). A rule base has been built to classify thematic mapper data into Eucalypt forest types been integrating terrain features (Skidmore, 1989).
In this paper, an attempt has been made to build a knowledge based system which can assist a user to find the potential groundwater region using a segmented Remote Sensing imagery and related geo-hydrological data that can be provided by GIS. The system works on backward chaining control strategy and Bayesian theory is applied for supporting the hypothesis based on evidences. The system is prepared on a PC based shell and has necessary explanation facility.
