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Using ESTA to develop expert system for the Natural Resource Management
Rajkishore Prasad
Lecturer University Deptt of Electronics, BRA Bihar University, Muzaffarpur.
Kumar Rajeev Ranjan
Lecturer, Deptt of Physics, College of Commerce, Patna
A. K. Sinha
Professor, Deptt of Electrical Eng MIT Muzaffarpur
M. M. Prasad
Deptt of Electronics, Lecturer, L. S. College Muzaffarpur
Introduction
The discipline of remote sensing is concerned with the collection of information
related in some way to the earth’s natural resources or environment without coming
into physical contact with them, through analysis of the acquired data [1]. In the
modern age of computing our most of the activities is influenced directly or indirectly
by a computer. The days when computer were confined to only scientific community
to do hard calculations are gone. This era is the era of expert skill. Everywhere in
every domain relevant expertise is the most wanted thing. The major problems in
accessing a human expert in a particular field are unavailability and scarcity of real
experts and if a human expert is available then there may be problem for common
people in making contact with him. Consultation may be very expensive and human
expert may feel the repetitive job uninteresting. This in turn may affect expert’s
efficiency.
The other major problems that are being faced by a human expert are the
limitation of his memory and processing inability of all the essential knowledge and
information required in the process of decision-making. As a result of researches and
developments, day by day, new knowledge in enormous amount is being added in
every discipline and thus more relevant and accurate advice can be taken from a
human expert if his/her own knowledge is accordingly updated which is not an easy
task. Human experts are bounded by some humane limitations and it is quite
impossible for a human expert to consider all the essential factors while taking
decision. Something is always escaped and remained unconsidered. Thus some
computer based tools or assistances like Decision support system, Decision making.Map
India 2003,6 th Annual International Conference and Exhibition, NewDelhi, India 2
system, Expert system etc. are needed even for an expert to update his knowledge and
get help in decision-making process. In this respect expert system has been proved to
be a very useful tool. Expert systems of today support many problem solving
activities such as decision making, knowledge fusing, designing, and planning,
forecasting, regulating, controlling, monitoring, identifying, diagnosing, prescribing,
interpreting, explaining, training etc. using different techniques and it is expected that
future expert systems will support even more activities. In the beginning, expert
systems were developed in the chemical and scientific domains and by the end of
1970s expert systems were operating in the medicine, chemical, education, natural
resources and science domains. Expert system started to gain popularity in the early
1980s[2].
The announcement of successful operational systems like PROSPECTOR a
natural resources system that evaluates geographic sites for potential mineral deposits
of commercial interest, MYCIN, medical consulting system, etc. catalyzed the expert
system technology [3,4]. The availability of powerful tools to develop expert system
has made possible creation of large number of expert systems in different domains in
general and for the natural resource management in particular [5]. Multi spectral
Remote Sensing data are used for land use and land cover classification. A simple
visual interpretation of remotely sensed imagery can reveal considerable detail on the
nature and distribution of land use and land cover in the area of interest. To visually
interpret digital data such as satellite images, individual spectral bands must be
displayed simultaneously in the form of a color composite. For example, IRS-1B
bands 1,2,3 (or Landsat TM bands 1, 2,3) broadly represent the blue, green and red
parts of the electromagnetic spectrum (ES). When these bands are fed through the
corresponding blue, green and red “color guns” of a computer monitor, the resulting
image strongly resembles what our eyes would see. Such images are called true color
composites (TCC).
Data taken in some other bands contains more information, which
may not be explicit in these visual bands. Such non-visual bands are arbitrarily
assigned one of the three primary colors (Red, Green, Blue) of visual bands. The
images so produced do not resemble to true color of the ground reality and are called
FCC images. However, false colors can still be related to land use and land cover of
interest. In general, multi spectral data contains more channels and only three of them
are required for the false color image. This results in unused information (channels) of
the original data. So usually multiple FCC images are used. However it is possible to
generate an image that is capable to reflect all the channels of data based on
compression of multi channels into three channels [22]. Two types of classification
techniques for FCC images exist: automated classification and visual interpretation.
Visual interpretation is usually carried out based on a false color image. Visual
interpretation is the identification of features based on their color, tone, texture and.Map India 2003,6 th Annual International Conference and Exhibition, NewDelhi, India 3
context within the imagery.
In this paper our main concern is with visual photo interpretation
method of IRS-1B based standard FCC and representation of such knowledge in
hypothetico-deductive framework. We implement the same in the development of a
rule-based expert system, using expert system shell ESTA (Expert System shell for
Text Animation), for beginners to learn the visual interpretation skill for the purpose
of land use & land cover mapping.
Expert system and visual interpretation of FCC image
The development and application of expert system in any discipline
is governed by different factors like the availability of human experts, scarcity or
rareness of experts, need of finding solution of problems unsolvable to single expert
etc. Many image interpretation systems have been developed in the past. These
systems are different in number of ways like the knowledge representation scheme,
control strategy, application area etc. Most of them are based on spectral features
of the image. Very few systems have been developed for developing visual
interpretation skill. In the field of land use /land categorization using satellite data a
rule based expert system [6] was developed using visual interpretation keys developed
by NRSA (National Remote Sensing Agency), India. In other development an expert
system for the landform study has been created, in the Prolog language, for landform
type identification and classification in automated and user interactive way [7].
Majority of systems have been developed for automatic image interpretation e.g. the
system SPAM [8] for aerial image interpretation is based on production rules. The
system MESSIE [9] has used production rules as well as semantic nets. In AIDA
system [10] semantic nets and rules has been used to represent knowledge base while
ERNEST [11] uses semantic net only. SIGMA [12] contains three expert modules
based on frame and rule representation. Recently Jenson et al [13] in his neural
network based photo interpretation approach have emphasized use of hybrid approach
i.e. visual interpretation and digital interpretation. However, in the neural network
based system the classification rules are hidden in its weight and is not useful for a
user for learning point of view.
Expert system Developmet in ESTA
Expert systems 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. 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 [14]. This led the.Map
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