ASPRS/ACSM (1994), copyright ASPRS/ACSM
Different remote sensing products should participate in exploring various geological and mineral resources. Provided that proper imagery is selected for intended applications, valuable and cost effective investigations can be achieved. In all cases, however, one of the most important factors required to reach correct interpretation is the expertise factor. That is, a human expert in image analysis and interpretation is an indispensable factor that must participate in the process of knowledge acquisition. This factor, however, is expensive or, even worse, unavailable in less fortunate parts of the world. The main objective of this research is to introduce possible artificial intelligence techniques that will participate in the interpretation stage of remote sensing geology. After giving the main concepts and motivations of using expert systems in remote sensing geology, an experimental prototype expert system was developed using a small domain dependent problem. The system c! onsists of a resident knowledge base that can totally or partially activated as a working memory. The knowledge base was developed as a rule based system using a LISP based language in a frame representation. The system was tested and found to be promising in remote sensing geology.
Key Words: Remote sensing geology, artificial intelligence, knowledge, expert systems, interpretation, expertise, and photogeology.
Interpretation of geology from space products is a well known process that requires human experts to conduct it. That is, a human operator who gains the skills of interpreting geologic features from different space products during many years of experience is a qualified person to conduct the task of geologic interpretation. This task is different from other interpretation tasks. For instance, most people can look at an aerial photograph and point out trees, highways, urban areas, and many other features. However, only few people (experts) can look at radar or TM images and interpret granite or basaltic flows. Accordingly, expertise is an important factor in geologic interpretation. Experts, however, are not available, very rare, or very expensive. Consequently, knowledge transfer is essential to many parts of the world. The most cost-effective method to accomplish such an objective is by coding the knowledge in an Artificial Intelligence (AI) system.
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Therefore, the main objective of this study is to introduce expert systems to geologic image interpretation. That is, developing an expert system that can report automatic interpretation for different geologic structures based on image analysis. Accordingly, an expert system called "Geologic Image Interpretation Expert System" (GI2EXS) is developed in this research. To conduct the concepts of this research in a clear and understandable manner, a prototype system with finite domain is considered. The next section highlights photogeology and remote sensing geology. Following that is a section concerning the main concepts and definitions of expert systems. Next is a section regarding detailed steps and methodologies of developing GI2EXS. Before the conclusions the results of testing the developed system are reported.
Geology is one of the most important sources of our planet that has direct impacts on human activities and wealth. Accordingly, methods of monitoring and interpreting earth geology have been realized as early as the invention of aerial cameras. Books and other publications in this matter were published as early as 1941. For instance, "Aerial photographs: their use and interpretation" is a revised publication of "Interpretation of geologic maps and aerial photographs" that was published in 1941 by Prof. A.J. Eardley. Similarly, "Aerial photographs and their applications" is a book by H.T. Smith that was published in 1943.
Photo-interpretation has been realized as an important source of knowledge since World War II. One of the most important fields of photo-interpretation is photogeology. Photogeology is defined as "the visual extraction of geological information from a photograph or image (Robinove, 1963) by conventional photo-interpretation instruments and techniques" (Williams, et al., 1983). Photogeology was used extensively during the 1950s as a search tool for oil and gas.
Geological remote sensing concerns techniques and principles of image interpretation. They include the manual (optical) and digital analysis of all products of available sensors. These analysis cover the electromagnetic spectrum regions relevant to geology. The knowledge acquired in these regions is collected by either passive or active sensors. The regions of valuable knowledge in the electromagnetic spectrum range from the ultraviolet through the visible, infrared, and into the radar microwave wavelengths.
Remote sensing geology has seen very rapid development and improvement on two main bases. The first is the theory of interpretation. That is, the theoretical aspects of image interpretation has been improved from the view point of qualitative and quantitative processing of imagery. Numerical analysis of geologic data is well established (Davis, 1986). The second is the technical tools and supporting instruments, which have been improved dramatically. This can be seen in the fast development of computer technology and image acquisition sensors.
It is important, however, to realize the fact that most remote sensing products are available only in 2-D digital formats. This fact makes other 3-D products (Aerial photographs) more attractive and reliable for geological studies. However, remote sensors that provide 3-D multi-spectral images of the earth surface is emerging.
Knowledge-based systems are sometimes referred to as expert systems. Accordingly,
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in this study both terms are used interchangeably. Expert systems consist of the applied and experimental aspects of AI science. AI is defined as "the study of how to make computers do things which at the moment people do better" (Rich and knight, 1990). People and computers are the two main factors that are incorporated in this definition. The human part of the definition concerns the expertise while the machine part concerns knowledge transfer.
Expert systems contain the same factors (human and machine resources) mentioned above. Accordingly, they are considered as the practical modeling of the findings of the AI science. Many basic texts in AI provide different definitions for expert systems (e.g., Friederich, et al., 1989; Rich, et al., 1991; Fieschi, 1990; Jackson, 1986). Among these definitions is the definition given by Bowerman and Glover, 1988. They defined an expert system as: " a system of software or combined software and hardware capable of competently executing a specific task usually performed by a human. Expert systems are highly specialized computer systems capable of stimulating that element of a human specialist's knowledge and reasoning that can be formulated into knowledge chunks characterized by a set of facts and heuristic rules."
As a historical background, AI science concentrated on psychological modeling and search strategies during the 1950s (Chandrasekaran, et al., 1990). In late 1960s expert systems started to gain solid foundations. Accordingly, very sophisticated inference engines and search techniques were developed. These improvements in applied AI field were based on game playing as the typical problem based on which expert systems were developed.
Later in 1970s many engineering and medical fields started to realize the importance of expertise and knowledge transfer. Accordingly, people in these fields developed their own expert systems that were distinguished from those developed by AI people. The basic difference lies in the types of problems that were treated in each field. For instance, diagnostic medical expert systems were developed by people in medicine while game playing expert systems were developed by people in AI science.
It was noticed that the expert systems that were developed by non-AI people are very successful systems with simple inference engines and search techniques. Only then, AI people started to review the major concepts of developing expert systems. They realized that the quality of the expert systems are influenced by the quality of the knowledge. Since then the concept of separating knowledge base from the inference engine become an important indication of successful expert systems.
Based on the findings of AI and other fields, highly ranked expert systems are developed and commercially represented (Edmunds, 1988). These systems are found to be of practical value to AI research and to engineering and medical fields, as well. Expert systems continue to build on, and contribute to, AI research by testing the strength of existing methods and defining their limitations (Barr, Cohen & Feigenbaum, 1989).
Geologic expert systems are very few if there is any (see Morris, 1991). There are, however, few expert systems that are dealing with interpretation of landforms based on different types of imagery (Al-garni, 1992 and Argialas, 1989). These systems are found to be very essential to many parts of the world where experts are not available. Many other expert systems that interpret different features from images are developed (Barnaba, et al., 1991). A good review of these systems can be found in June issue
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of 1990 of Photogrammetric Engineering and Remote Sensing.
This system was developed according to the major concepts known in AI fields for developing standard expert systems. The following list is summarizing the steps that were followed to develop the system:
These five steps are detailed in the next few sections.
The problem that is treated in this research is defined as introducing artificial intelligence techniques to the field of geologic image interpretation by developing an expert system that can interpret geologic structures. Accordingly, there are two parallel tasks that are considered in this research. The first task is the development of an AI program that fits the purposes of geologic investigations. It is accomplished according to the five major steps that are listed above. The second task is the definition of the domain of the system.
The second task, however, is important to be discussed now. Expert systems are known to be domain dependent systems. That is, an expert system is designed to treat a specific problem. For instance, an expert system can be designed to interpret certain land cover while another can be developed to interpret certain land use. That is, there is no one giant expert system that can interpret all features and treat all problems in geology.
Accordingly, a finite domain expert system is developed in this research. That is, identification of geologic structures from topographic clues based on image analysis is the specific problem that this research treats. Today, geologic structures become an important part of geologic mapping. Our objective here is to define these structures. They are revealed by topographic parameters as well as by many other parameters that are explained in a later section. As soon as the geologic structures are identified many inferences regarding tectonic processes can be obtained by geologists. Among different types of knowledge that can be obtained by defining geologic structures are deformations, scale of deformations, types of different mechanisms, motions and stress, and many other tectonic processes (Gold, 1980). It is out of the scope of this research to include these inferences in the developed expert system. Only interpretation of geologic structures are contained in the current s! yste m. However, the authors are studying possible techniques for modeling and deducing important geologic knowledge based on interpreted geologic structures.
As can be realized, the ideal solution to the problem of identifying geologic structures is to have a human expert participate and investigate the problem. However, this ideal case is not possible except in few rich places in the world. Therefore, alternative solutions should be considered.
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According to the ability of the human being in modern times, the best alternative is probably lying on the part of the artificial intelligence techniques. That is, a human expert can be replaced, to a certain extent, with a large degree of success if AI theories and techniques are applied. Therefore, an expert system is a proposed solution to this problem (identifying geologic structures).
For the purpose of this study, image interpretation is classified into two inter-related categories (see figure 1). The first category is geological interpretation using photographs. It is called photogeology. The second category is geological interpretation using remote sensing. It is called remote sensing geology. photogeology is divided into two further groups. The first group is called a macro-interpretation which uses close range photogrammetry and ground investigation to make detail acquisition of geological knowledge. The second group is called a micro-interpretation where aerial photographs of large to medium scales are involved in knowledge acquisition of geological features. In general, the first category (photogeology) deals with small portion of the earth's surface.
By the same analogy, remote sensing geology is classified into two groups. The first group deals with small scale imagery which covers wide area of earth's surface but retains an acceptable resolution. The second group includes all other space imagery that have very small image scale, cover very large portion of the earth surface, and have very low resolution. In general, this category (remote sensing geology) can be looked at as a reconnaissance survey for the first category (photogeology). That is, interpretation and delineation of large geophysical units and landforms are accomplished under the concepts and theories of the second category. These preparations and classification schemes are expected to economize and strengthen the system.
The costs of implementing some AI techniques require careful investigation (Friederich, et al., 1989). If the cost of developing AI systems is more than that of hiring human experts, then developing AI systems may have no economic advantages. In this regard, tools and human sources are two main factors that may affect budget
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considerations. In general, these two factors are still very cost-effective if compared with costs of hiring human experts who are either very expensive or not available. This is true since programmers are not very rare and tools or computer technologies are going down in prices. For instance, a PC-computer with proper expert system shells can be used by a knowledge engineer to provide excellent domain dependent expert system at very acceptable costs.
In this research, the main objective is to highlight some concepts of developing expert systems. Accordingly, an expert system shell in a frame-based representation with a LISP-based programming tools were found to be good enough to accomplish the themes of this study. These were integrated with a PC-based data base and small image processing packages to produce the final expert system model.
The conceptual system and solutions are illustrated in figure 2. The system consists of independent models that are integrated to give an interactive system which can report proper consultations automatically based on minimal data acquired from the end users. That is, the developed system contains a data base with relational data and knowledge acquired from relevant independent models to report the identity of the geologic structure under consideration. The coded knowledge are acquired from real cases that are accomplished by experts in the field.
In this research two important types of knowledge acquired from different sources are combined to reach acceptable automatic interpretation. The first type of knowledge is obtained from processed digital images. Image enhancement for geological interpretation purposes is accomplished using digital image processing techniques. The second type of knowledge is obtained from ancillary data and expertise. It is usually acquired by human operators (knowledge engineers, in our case) from human experts and well documented publications. These data are integrated to produced a piece of knowledge. After enough knowledge is acquired a hypothesis is reached. The hypothesis is verified or rejected based on the collected evidence. In case of a hypothesis is verified, a feature identification is inferred by the expert system.
One of the most important and time-consuming phases of developing an expert system is the knowledge acquisition phase. This stage was accomplished in four months. To attack this problem effectively, two important concepts are considered:
The later concept concerns the suggestion of best remote sensing data for particular applications. It deals with remote sensors and their applicability to different geologic investigations. This concept has been fully implemented by the first author of this
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paper in another research in this conference. Therefore, no further discussion on this matter is represented in this paper. As an introduction to the possibility of combining image processing data, ancillary data, and human expertise in one system, the domain of this system is limited to two main elements:
The ultimate objective of remote sensing geology is to provide the analyst with basic geologic information regarding surface lithology and geological structure of the surface. This is appropriately achieved if and only if proper discrimination between different rocks is attained. To reach this goal, geological attributes (parameters) that can provide unique knowledge should be collected.
Accordingly, an important factor in developing geological expert systems is the definition
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of proper parameters, attributes, and values based on which different rocks are interpreted. Since this factor is purely dependent on experimental works, skillful human experts in interpreting rocks from remote sensing data should participate. Expertise, books, publications, case studies, and reports are considered in this investigation to define proper attributes. Table 1 shows a sample of knowledge acquired from different sources.
An expert system is called so because it should contain the expertise. That is, it should behave in the same way that the human expert does while solving a similar problem. In other words, the system should be able to search for a solution for the problem under consideration according to the search methods and logic of a human expert. It has been noticed by many scientists that human experts start with very vague idea (forward search), collect enough evidence to establish a hypothesis, and continue to collect more clues and evidence (backward search) to verify the hypothesis (reach a solution or a goal) or to reject it (Way, 1973 and Al- garni, 1992). The exact strategy was developed in this expert system. That is, developing a forward search control until a goal is established and the rest of the control strategy is a backward chaining search.
The acquired knowledge was classified into eight classes based on resolution criteria.
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These classes are illustrated in figure 1. They are produced from photogeology and remote sensing geology shown in the figure. The classes are numbered from one to eight in the figure. Consequently, eleven frames were developed each of which is containing a specialized knowledge. These frames are represented in a tree-like relational database to benefit from inheritance property found in AI techniques. Figure 1 shows the general scheme of the frames. In the figure, the frame called human work and investigations is not implemented yet. Each box in the figure represents a specialized frame. The box called small scale represents two frames (one for photogeology and the other for remote sensing geology).
To represent this knowledge in the expert system a simple coding and decoding scheme (CODES) is developed to simplify writing the program. The CODES used in this research has the advantage of reducing number of used characters in writing a rule. In a separate help facility a user can acquire the decoding routine to give elaboration and explanation of reported results.
CODES is quite advantageous specially in all hidden operations that are not seen nor required by the end users. Accordingly, more than 90% of system's contents that are classified as internal operations are coded in simple letters with no more than three digits each. Most of these operations are using dummy variables that are used to reach certain goals. Therefore, even the help facilities do not contain the decoding part of these internal operations because they do not mean any thing to the users.Table 2 shows a small portion of the library that contain the CODES
In this research, geologic structures are the main objective to be identified. Accordingly, possible clues that are expected to reveal the identity of geologic structures of a region were investigated. Since topography of lands reflect the underlying geology, more emphasis were put on topographic aspects (e.g., DEM, shape, slope, etc...) of different land surfaces. [End Page 55]
As soon as topographic clue is identified, few other knowledge pieces are acquired to develop certain hypothesis about landform identity. If the hypothesis is not rejected, then the identity of the landform is reported with certain confidence level. Once the identity of a landform is known, the geomorphology of the study area can be inferred. Afterwards, there are known relationships between landforms, geomorphic aspects, and geologic processes. For instance, the forms or the configuration of the earth's surface are castled or shaped by eight major processes. These are running water, glaciers, groundwater, wave and currents, wind, weathering, volcanism, and diastrophism (deformation of the earth's crust) (Easterbrook, 1969 and Powers, 1966). As seen in table 1, attributes (sometimes called parameters) are defined as a unique or partially unique property of different geologic features. These attributes are global in nature but are specialized by attribute values. For instanc! e, t he parameter slope is a global parameter that can be given to synclinal and anticlinal folds at the same time. However, +35 degrees and -35 degrees are two different values for that parameter based on which these two folds are distinguished.
Figure 3 shows a representation of unique or partially unique parameters that were selected for this research. These parameters may be expanded to include more parameters and more attribute values. However, this requires a development of a hybrid system which can be developed based on the same concepts that are provided in this study. The only difference is the requirement of more resources to develop hybrid systems.
The developed expert system was tested to evaluate its workability and accuracy. The workability test is a comprehensive test based on which the AI program was debugged. The knowledge engineer provided the system with all input and all possible alternatives based on which most rules were fired and all frames were instantiated. In this test most solutions that the data base contained were tested for possible blunders and errors. Only few errors were detected and corrected. Errors were traced by
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comparing expected solutions with reported solutions. Accordingly, the workability test was accomplished successfully and the system is behaving as expected.
The other test is the accuracy test. The main objective of this test was to evaluate the compliance of the identities reported by the system with those provided by human experts for the same problems. For this purpose a total of three tests were conducted. These tests were performed on two phases. The first phase was accomplished by experts in photogrammetry, remote sensing, and geology. The second phase was performed by a knowledge engineer. The method of testing is explained next.
The knowledge engineer selected an area in Saudi Arabia. The area was selected to have different geologic features. A map for the selected area was acquired as well as different images and aerial photographs. Some of the acquired data are in digital format. The data that were in digital format were processed by suitable image processing tools to extract the required input for the expert system. Afterwards, the imagery and aerial photographs were given to the experts to reveal the identities of geologic structures found in the selected area based on certain parameters such as those listed in table 1. The experts were asked to state their confidence in the obtained results and rank the alternative solutions, if there are some.
The same parameter values were input to the expert system. It manipulated the input and reported the identity of the geologic structures in the area under consideration. The reported results were associated with certainty factors as an indication of the degree of confidence in the obtained results.
The results obtained by the expert system were compared with those obtained by the experts. The conclusions of the tests are listed in table 3. The absolute difference between the answers are listed in the sixth column to indicate the compliance of the conclusions obtained by the system with those obtained by the human.
In the first test, both the human expert and the expert system were able to identify faults with horizontal axes underlying series of parallel ridges and valleys in sedimentary rocks environment. The expert system, however, reported another geologic structure as a second preference with lower certainty factor. That is, faults with axes that are not horizontal in a sedimentary rocks environment were listed by the system as another possibility for the identity of the geologic structure under consideration. This test indicated a similarity coefficient of 0.98 between the identity reported by
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the expert system and by the human expert.
In the second test, the system was more confident than the human expert in reporting the identity of the geologic structure see table 3). On the other hand, the human expert was quite superior to the system in reporting the identity of the third geologic structure the identity of ridges developed on steeply tilted resistant beds).
Interpretation of geologic structures from photographs and images is realized to be an AI problem due to the large amount of expertise required to reveal the identities of these structures. Therefore, expert systems are introduced to the field of geologic interpretation. There are many variables that should be considered while designing expert systems for geologic interpretation. Variables include types of sensors, kind of geologic structures, climate conditions, and resolutions required to identify the attributes of the features. These factors affect the design of the systems and their quality.
The developed expert system in this study is quite promising and encouraging. It helps illustrating the major concepts of developing similar (prototype of hybrid) systems. The conducted tests showed an acceptable behavior of the system. Its accuracy is, also, very comparable to that of a human achievement. A general correlation factor shows a 0.957 similarity between the human expert and the system with an absolute average difference of 4.3% between them.
Further research is recommended to standardize the effective geologic parameters for image interpretation purposes. Automation of the process of identification of geologic structures should be further investigated using different artificial intelligence techniques such as neural networks.
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