Knowledge - based Approach to Update Landuse Layer of an Operational GIS
Amarsaikhan. D
Informatics Center Mongolian Academy of Sciences
Gorte. B, Nieuwenhuis. W, Bakker. W
International Institute for Aeorspace Survey and Earth Sciences. Enschede. The Netherlands
Abstract
A knowledge-based approach was used to extract information of land cover types and update landuse layer of an operational GIS. For this purpose was designed a prototype Knowledge-based System (KBS) connected with the RS/GIS system. Whilst the RS/GIS is used to store spatial and non-spatial information and procedures to be used in data manipulation, the KBS is used for decision making on the basis of the gathered knowledge. For the decision making, Gaussian maximum likelihood classifier is used and prior class probabilities are stored within the KBS.
Introduction
Supervised of multispectral remotely sensed data has assumed an increasing importance as an automatic means for land cover mapping, and a great number of investigations have dealt with the application of diverse statistical procedures for discrimination between the cover types of a territory on the basis of their spectral signatures. One of the widely used supervised classifiers is the maximum likelihood decision rule which gives best results by incorporating prior probabilities. Using this technique for the classification of land cover types the same set of training samples whose conditional probabilities are likely to change depending on what cover type is present in a given season can be used. This approach can successfully be applied within an operational GIS, where livelihoods for the state of objects are stored and prior probabilities are defined from the layer (e.g. landuse) of interest according to the context and time. The main aim of the study is to solve this problem within the domain of a knowledge based approach which will require in this case two kinds of knowledge, that is knowledge about conditional probabilities and analysis tools (i.e. techniques to be used).
For that purpose, a prototype KBS linked to a RS/GIS system (e.g. ILWIS) was developed RS/GIS: The function of an information system is to improve a user's ability to make decisions in research, planning and management. An Information system involves a chain of steps: from observation and collection of data, through their analysis, to their use in some decision making process. GIS is computer-based technology, used to input, store, retrieve at will, analyze and output referenced data. This is normally considered to involve a georeferenced computer database and appropriate hardware and application software Primary GIS were mostly used to store and analyze vector objects which are defined by points, lines. And polygons according to the locations and attribute information referred to their characteristics. Present so call RS/GIS systems effectively handle, process and analyze remotely sensed data and have facilities to integrate with the cartographic information stored in vector structure, thus making a promotion in spatial analysis and decision making.
However, retrieving and analyzing parts of these systems is slow and labour consuming. In order to avoid these problems one can design and add to a RS/GIS a KBS to make a rapid decision. Knowledge - based system : A knowledge - based system is mainly a set of computer programs used to make important decisions in a selected narrow field on the basis of gathered knowledge. It consists of the knowledge Base (KB), the Inference Engine (IE) and the User Interface. The KB contains a collection of specific and general facts and descriptions of relations between them. Knowledge representation is the main step in the development of a KB and widely used structures are frame-based and production rule types. In rule-based system for the demand of a user, who is communicating via the Use interface, the Inference engine implements a search within the Knowledge base by the use of defined rules to find answers. There are two reasoning techniques used in production rule systems: forward -chaining and backward-chaining inferences. Forward chaining starts with the evidence, chaining the rules towards the hypothesis. It is the preferred technique if the observations are to be readily available, for instance in Remote Sensing and GIS. In pattern recognition most classification algorithms are based on the forward chaining principle. Backward chaining starts with the most likely hypothesis and searches in the KB facts which can support that hypothesis. In this way only those observations which are necessary to support the hypothesis are treated. It is very applicable if the domain of observations is large. In the Frame-based system an object is represented by a frame, in which the basis storage unit is the slot. A slot is used to store knowledge and procedures to be used. In comparison with rule-base the frames can be arranged in hierarchy, maintaining inheritance, which is a powerful way of retrieving attribute values and getting the same information from a variety of sources. The introduction of KB approach to the spatially-oriented systems will have a number of advantages, e.g. it will promote rapid decision making and simplify procedures to be used.
