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IRS-1B based FCC image interpretation for land use and land cover mapping: An expert system approach
Expert System Development in ESTA
ESTA Expert system came out as the first commercial fruit of AI. These are domain dependent computer programme that use knowledge and inference procedure of domain specific human expert to mimic his/her problem solving capability and way in which they solve [17]. In the beginning the expert system development was mainly carried out by the AI people using sophisticated inference engine and search techniques. However, later it was found that the expert system developed by non-AI (domain expert) people using simple inference engine and search techniques outperformed those developed by AI people. This led the AI people to realize the fact that the quality of knowledge highly influences the quality of expert system, which in turn led to the separation of knowledge module from inference and control strategies [18]. This gave a great leap in the expert system technology and resulted in the development of expert system shell. An expert system in the context of expert system shell may be defined as follows: EXPERT SYSTEM=KNOWLEDGE BASE + EXPERT SYSTEM SHELL (Control &Inference +User Interface). The separation of the control and inference parts in the knowledge base is a fundamental feature of an expert system shell. ESTA is also an expert system shell developed by PDC (Prolog Development Center), Denmark [19]. By providing with it a knowledge base ,as shown in fig.1, of a specific domain an expert system can be created. Thus KNOWLEDGE BASE +ESTA= EXPERT SYSTEM.  Fig. 3: A Screen shot when user seek help during consiltator ESTA system has several advantages over other expert system shell like CLISP. According to PDC, ESTA is easy to use and a great stand-alone environment for constructing advisory and decision support systems. Building advanced knowledge base with ESTA requires no previous programming experience and it is suitable for many problem domains. ESTA is the perfect tool for structuring of knowledge. It includes explanation facilities for the question being asked and for given advice. Details about ESTA can be found in the reference [19] or PDC homepage. It is very easy to use, fast and requires no programming experience. The knowledge base of the ESTA consists of rules represented in its own syntax. It has all the inbuilt facilities to write the rules that build the knowledge base. Table 1
Knowledge Base Development Palpably as an expert system shell, ESTA for knowledge engineer works as tool to create knowledge base and from user side it is used to consult the created knowledge base as shown in the fig.1.Thus the task of developing expert system using ESTA, domain independent module, is centered at developing a domain specific knowledge base, which is a multi-step process. These steps are problem identification, knowledge acquisition and knowledge representation in accordance with the syntax of ESTA. Problem identification is already contained in the previous sections. The rest two are described below. Knowledge Acquisition The knowledge required for present study has been gleaned out from available standard literature [3,4,20] and human experts from remote sensing center, Patna using IRS-1B based FCC images. The theme of such interpretation rules, in very brief, for land cover and land use identification is presented in the table 1 The IRS-1B satellite uses Linear Imaging Self Scanner (LISS) I & II sensors, which are approximately equivalent to Thematic Mappers (TM) bands 1,2,3& 4. . So the same knowledge base can also be used to interpret TM based standard FCC image with little variation Knowledge Representation The knowledge base for ESTA consists of simple if- then rules. It is also obvious from above table of visual image interpretation key that this domain is based on the specific rules for specific land use/land cover category, so the rule based approach [21] for knowledge representation will be best suited. There are two major knowledge representations in ESTA namely Section and Parameters. Section is top level of knowledge representation and contains the logical rules that directs ESTA how to solve problem, actions to perform such as giving advice, going to other section, calling to routines etc. The first section is always named as start section. Parameters are used as variable and it decides the flow of control among the sections. ESTA accepts four types of parameters namely Boolean or Logical, Text, Number and Category parameters. These parameters serve different purposes. The Boolean parameter is used when the answer to asked question is either Yes, No, or Unknown. Text parameters are used for text object. Number parameter is used to represent numerical values. Category parameters are used when variable takes more than one values. The value for any of the parameter is calculated from end-user’s response to a question, through other parameters or as a result of application of rules. Any parameter consists of declaration field, type field and number of optional field such as explanation field, rules field, picture field, question field etc. |