Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC 29208

Dr. John R. JensenDr. John R. JensenDepartment of GeographyDepartment of Geography

University of South CarolinaUniversity of South CarolinaColumbia, SC 29208Columbia, SC 29208

Dr. John R. JensenDr. John R. JensenDepartment of GeographyDepartment of Geography

University of South CarolinaUniversity of South CarolinaColumbia, SC 29208Columbia, SC 29208

Thematic Information Extraction: Thematic Information Extraction: Artificial Intelligence Artificial Intelligence

Thematic Information Extraction: Thematic Information Extraction: Artificial Intelligence Artificial Intelligence

Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence

““the study of how to make computers do things which, the study of how to make computers do things which, at the moment, people do better”.at the moment, people do better”.

But how do we know when an artificially intelligent system has been created? We But how do we know when an artificially intelligent system has been created? We could use the could use the Turing testTuring test,, which suggests that if we are unable to distinguish a which suggests that if we are unable to distinguish a computer’s response to a problem of interest from a human’s response to the same computer’s response to a problem of interest from a human’s response to the same problem, then the computer system is said to have problem, then the computer system is said to have intelligenceintelligence. The test is for an . The test is for an artificial intelligence program to have a blind conversation with an interrogator for artificial intelligence program to have a blind conversation with an interrogator for 5 minutes. The interrogator has to guess if the conversation is with an artificial 5 minutes. The interrogator has to guess if the conversation is with an artificial intelligence program or with a real person. The AI program passes the test if it fools intelligence program or with a real person. The AI program passes the test if it fools the interrogator 30% of the time. Unfortunately, it is very difficult for most the interrogator 30% of the time. Unfortunately, it is very difficult for most artificial intelligence systems to pass the Turing test. For this reason, “the field of artificial intelligence systems to pass the Turing test. For this reason, “the field of AI as a whole has paid little attention to Turing tests,” preferring instead to AI as a whole has paid little attention to Turing tests,” preferring instead to forge forge ahead developing artificial intelligence applications that simply workahead developing artificial intelligence applications that simply work..

““the study of how to make computers do things which, the study of how to make computers do things which, at the moment, people do better”.at the moment, people do better”.

But how do we know when an artificially intelligent system has been created? We But how do we know when an artificially intelligent system has been created? We could use the could use the Turing testTuring test,, which suggests that if we are unable to distinguish a which suggests that if we are unable to distinguish a computer’s response to a problem of interest from a human’s response to the same computer’s response to a problem of interest from a human’s response to the same problem, then the computer system is said to have problem, then the computer system is said to have intelligenceintelligence. The test is for an . The test is for an artificial intelligence program to have a blind conversation with an interrogator for artificial intelligence program to have a blind conversation with an interrogator for 5 minutes. The interrogator has to guess if the conversation is with an artificial 5 minutes. The interrogator has to guess if the conversation is with an artificial intelligence program or with a real person. The AI program passes the test if it fools intelligence program or with a real person. The AI program passes the test if it fools the interrogator 30% of the time. Unfortunately, it is very difficult for most the interrogator 30% of the time. Unfortunately, it is very difficult for most artificial intelligence systems to pass the Turing test. For this reason, “the field of artificial intelligence systems to pass the Turing test. For this reason, “the field of AI as a whole has paid little attention to Turing tests,” preferring instead to AI as a whole has paid little attention to Turing tests,” preferring instead to forge forge ahead developing artificial intelligence applications that simply workahead developing artificial intelligence applications that simply work..

Jensen, 2005Jensen, 2005Jensen, 2005Jensen, 2005

Artificial Intelligence Artificial Intelligence

Artificial intelligence research was initiated in 1955 when Artificial intelligence research was initiated in 1955 when Allen Allen NewellNewell and and Herbert SimonHerbert Simon at the RAND Corporation proved that at the RAND Corporation proved that computers could do more than calculate.computers could do more than calculate.

““They demonstrated that computers were They demonstrated that computers were physical symbol systemsphysical symbol systems whose symbols could be made to stand for anything, including whose symbols could be made to stand for anything, including

features of the real world, and whose programs could be used as features of the real world, and whose programs could be used as rules for relating these features. In this way computers could be used rules for relating these features. In this way computers could be used

to simulate certain important aspects of intelligence. to simulate certain important aspects of intelligence. Thus, the Thus, the information-processing model of the mind was born”.information-processing model of the mind was born”.

Artificial intelligence research was initiated in 1955 when Artificial intelligence research was initiated in 1955 when Allen Allen NewellNewell and and Herbert SimonHerbert Simon at the RAND Corporation proved that at the RAND Corporation proved that computers could do more than calculate.computers could do more than calculate.

““They demonstrated that computers were They demonstrated that computers were physical symbol systemsphysical symbol systems whose symbols could be made to stand for anything, including whose symbols could be made to stand for anything, including

features of the real world, and whose programs could be used as features of the real world, and whose programs could be used as rules for relating these features. In this way computers could be used rules for relating these features. In this way computers could be used

to simulate certain important aspects of intelligence. to simulate certain important aspects of intelligence. Thus, the Thus, the information-processing model of the mind was born”.information-processing model of the mind was born”.


Unfortunately, Unfortunately, artificial intelligence was oversold in the 1960s much artificial intelligence was oversold in the 1960s much like remote sensing was oversold in the 1970s.like remote sensing was oversold in the 1970s. General artificial General artificial intelligence problem solving was found to be intelligence problem solving was found to be much more difficult much more difficult than originally anticipatedthan originally anticipated. Scientists could not get computers to . Scientists could not get computers to solve problems that were routinely solved by human experts. solve problems that were routinely solved by human experts. Therefore, scientists instead started to investigate the development of Therefore, scientists instead started to investigate the development of artificial intelligence applications in “artificial intelligence applications in “micro-worldsmicro-worlds,” or ,” or very narrow very narrow topical areastopical areas. This led to the creation of the first useful artificial . This led to the creation of the first useful artificial intelligence systems for select applications, e.g., games, disease intelligence systems for select applications, e.g., games, disease diagnosis (diagnosis (MYCINMYCIN), spectrograph analysis (), spectrograph analysis (DENDRALDENDRAL). NASA’s ). NASA’s REMOTE AGENTREMOTE AGENT program was the first on-board autonomous program was the first on-board autonomous planning program to control the scheduling of operations for a planning program to control the scheduling of operations for a spacecraft traveling a hundred million miles from Earth.spacecraft traveling a hundred million miles from Earth.

Unfortunately, Unfortunately, artificial intelligence was oversold in the 1960s much artificial intelligence was oversold in the 1960s much like remote sensing was oversold in the 1970s.like remote sensing was oversold in the 1970s. General artificial General artificial intelligence problem solving was found to be intelligence problem solving was found to be much more difficult much more difficult than originally anticipatedthan originally anticipated. Scientists could not get computers to . Scientists could not get computers to solve problems that were routinely solved by human experts. solve problems that were routinely solved by human experts. Therefore, scientists instead started to investigate the development of Therefore, scientists instead started to investigate the development of artificial intelligence applications in “artificial intelligence applications in “micro-worldsmicro-worlds,” or ,” or very narrow very narrow topical areastopical areas. This led to the creation of the first useful artificial . This led to the creation of the first useful artificial intelligence systems for select applications, e.g., games, disease intelligence systems for select applications, e.g., games, disease diagnosis (diagnosis (MYCINMYCIN), spectrograph analysis (), spectrograph analysis (DENDRALDENDRAL). NASA’s ). NASA’s REMOTE AGENTREMOTE AGENT program was the first on-board autonomous program was the first on-board autonomous planning program to control the scheduling of operations for a planning program to control the scheduling of operations for a spacecraft traveling a hundred million miles from Earth.spacecraft traveling a hundred million miles from Earth.


Artificial Intelligence Artificial Intelligence

Expert Systems Expert Systems Expert Systems Expert Systems

A knowledge-based A knowledge-based expert systemexpert system is defined as: is defined as:

““a system that uses human knowledge to solve problems that a system that uses human knowledge to solve problems that normally would require human intelligence”.normally would require human intelligence”.

It is the ability to “solve problems efficiently and effectively in a It is the ability to “solve problems efficiently and effectively in a narrow problem area” and “to perform at the level of an expert”. narrow problem area” and “to perform at the level of an expert”. Expert systems represent the expert’s domain (i.e., subject matter) Expert systems represent the expert’s domain (i.e., subject matter) knowledge base as data and rules within the computer. The rules and knowledge base as data and rules within the computer. The rules and data can be called upon when needed to solve problems. A different data can be called upon when needed to solve problems. A different problem within the domain of the knowledge base can be solved problem within the domain of the knowledge base can be solved using the same program without reprogramming. using the same program without reprogramming.

Knowledge-based expert systems are used extensively in remote Knowledge-based expert systems are used extensively in remote sensing research.sensing research.

A knowledge-based A knowledge-based expert systemexpert system is defined as: is defined as:

““a system that uses human knowledge to solve problems that a system that uses human knowledge to solve problems that normally would require human intelligence”.normally would require human intelligence”.

It is the ability to “solve problems efficiently and effectively in a It is the ability to “solve problems efficiently and effectively in a narrow problem area” and “to perform at the level of an expert”. narrow problem area” and “to perform at the level of an expert”. Expert systems represent the expert’s domain (i.e., subject matter) Expert systems represent the expert’s domain (i.e., subject matter) knowledge base as data and rules within the computer. The rules and knowledge base as data and rules within the computer. The rules and data can be called upon when needed to solve problems. A different data can be called upon when needed to solve problems. A different problem within the domain of the knowledge base can be solved problem within the domain of the knowledge base can be solved using the same program without reprogramming. using the same program without reprogramming.

Knowledge-based expert systems are used extensively in remote Knowledge-based expert systems are used extensively in remote sensing research.sensing research.


Domain (thematic) knowledge Domain (thematic) knowledge contained in an expert’s mind is contained in an expert’s mind is extracted in the form of a extracted in the form of a knowledge baseknowledge base that consists of that consists of hypotheseshypotheses, , rulesrules, and , and conditionsconditions that satisfy the rules. that satisfy the rules.

A A user interfaceuser interface and an and an inference inference engineengine are used to encode the are used to encode the knowledge base rules, extract the knowledge base rules, extract the required information from required information from online online databasesdatabases, and solve problems. , and solve problems.

Hopefully, the information is of Hopefully, the information is of value to the value to the useruser who queries the who queries the expert system.expert system.

Domain (thematic) knowledge Domain (thematic) knowledge contained in an expert’s mind is contained in an expert’s mind is extracted in the form of a extracted in the form of a knowledge baseknowledge base that consists of that consists of hypotheseshypotheses, , rulesrules, and , and conditionsconditions that satisfy the rules. that satisfy the rules.

A A user interfaceuser interface and an and an inference inference engineengine are used to encode the are used to encode the knowledge base rules, extract the knowledge base rules, extract the required information from required information from online online databasesdatabases, and solve problems. , and solve problems.

Hopefully, the information is of Hopefully, the information is of value to the value to the useruser who queries the who queries the expert system.expert system.

Components of a Typical Rule-based Expert SystemComponents of a Typical Rule-based Expert SystemComponents of a Typical Rule-based Expert SystemComponents of a Typical Rule-based Expert System

Jensen, 2005Jensen, 2005

Expert System User Interface Expert System User Interface Expert System User Interface Expert System User Interface

The expert system The expert system user interfaceuser interface should should be easy to use, interactive, and interesting. be easy to use, interactive, and interesting. It should be intelligent and accumulate It should be intelligent and accumulate user preferences in an attempt to provide user preferences in an attempt to provide the most pleasing communication the most pleasing communication environment possible. The figure depicts a environment possible. The figure depicts a commercially available commercially available Knowledge Knowledge Engineer interfaceEngineer interface that can be used to that can be used to develop remote sensing–assisted expert develop remote sensing–assisted expert systems. This expert system systems. This expert system shellshell was was built using object-oriented programming. built using object-oriented programming. All of the hypotheses, rules, and All of the hypotheses, rules, and conditions for an entire expert system may conditions for an entire expert system may be viewed and queried from the single be viewed and queried from the single user interface. user interface.

The expert system The expert system user interfaceuser interface should should be easy to use, interactive, and interesting. be easy to use, interactive, and interesting. It should be intelligent and accumulate It should be intelligent and accumulate user preferences in an attempt to provide user preferences in an attempt to provide the most pleasing communication the most pleasing communication environment possible. The figure depicts a environment possible. The figure depicts a commercially available commercially available Knowledge Knowledge Engineer interfaceEngineer interface that can be used to that can be used to develop remote sensing–assisted expert develop remote sensing–assisted expert systems. This expert system systems. This expert system shellshell was was built using object-oriented programming. built using object-oriented programming. All of the hypotheses, rules, and All of the hypotheses, rules, and conditions for an entire expert system may conditions for an entire expert system may be viewed and queried from the single be viewed and queried from the single user interface. user interface.


Creating the Knowledge Base Creating the Knowledge Base Creating the Knowledge Base Creating the Knowledge Base

Images, books, articles, manuals,Images, books, articles, manuals, and and periodicalsperiodicals have a tremendous have a tremendous amount of information in them. amount of information in them. Practical experiencePractical experience in the field with in the field with vegetation, soils, rocks, water, atmosphere, and urban infrastructure vegetation, soils, rocks, water, atmosphere, and urban infrastructure is also important. However, a human must comprehend the is also important. However, a human must comprehend the information and experiences and information and experiences and turn it into knowledgeturn it into knowledge for it to be for it to be useful. Many human beings have trouble interpreting and useful. Many human beings have trouble interpreting and understanding the information in images, books, articles, manuals, understanding the information in images, books, articles, manuals, and periodicals. Similarly, some do not obtain much knowledge from and periodicals. Similarly, some do not obtain much knowledge from field work. Fortunately, field work. Fortunately, some laypersons and scientists are some laypersons and scientists are particularly adept at processing their knowledge using three different particularly adept at processing their knowledge using three different problem-solving approachesproblem-solving approaches: :

Images, books, articles, manuals,Images, books, articles, manuals, and and periodicalsperiodicals have a tremendous have a tremendous amount of information in them. amount of information in them. Practical experiencePractical experience in the field with in the field with vegetation, soils, rocks, water, atmosphere, and urban infrastructure vegetation, soils, rocks, water, atmosphere, and urban infrastructure is also important. However, a human must comprehend the is also important. However, a human must comprehend the information and experiences and information and experiences and turn it into knowledgeturn it into knowledge for it to be for it to be useful. Many human beings have trouble interpreting and useful. Many human beings have trouble interpreting and understanding the information in images, books, articles, manuals, understanding the information in images, books, articles, manuals, and periodicals. Similarly, some do not obtain much knowledge from and periodicals. Similarly, some do not obtain much knowledge from field work. Fortunately, field work. Fortunately, some laypersons and scientists are some laypersons and scientists are particularly adept at processing their knowledge using three different particularly adept at processing their knowledge using three different problem-solving approachesproblem-solving approaches: :



1.1. Algorithms using conventional computer programs Algorithms using conventional computer programs

2.2. Heuristic knowledge-based expert systems: Heuristic knowledge-based expert systems: a.a. Human-derived rulesHuman-derived rulesb.b. Machine-derived rulesMachine-derived rules

3.3. Artificial neural networks Artificial neural networks

1.1. Algorithms using conventional computer programs Algorithms using conventional computer programs

2.2. Heuristic knowledge-based expert systems: Heuristic knowledge-based expert systems: a.a. Human-derived rulesHuman-derived rulesb.b. Machine-derived rulesMachine-derived rules

3.3. Artificial neural networks Artificial neural networks



Algorithmic Approaches to Problem Solving:Algorithmic Approaches to Problem Solving:

Conventional algorithmic computer programs contain little Conventional algorithmic computer programs contain little knowledge other than the basic algorithm for solving a specific knowledge other than the basic algorithm for solving a specific problem, the necessary boundary conditions, and data. The problem, the necessary boundary conditions, and data. The knowledge is usually embedded in the programming code. knowledge is usually embedded in the programming code. As As new knowledge becomes available, the program has to be new knowledge becomes available, the program has to be changed and recompiled.changed and recompiled.

Algorithmic Approaches to Problem Solving:Algorithmic Approaches to Problem Solving:

Conventional algorithmic computer programs contain little Conventional algorithmic computer programs contain little knowledge other than the basic algorithm for solving a specific knowledge other than the basic algorithm for solving a specific problem, the necessary boundary conditions, and data. The problem, the necessary boundary conditions, and data. The knowledge is usually embedded in the programming code. knowledge is usually embedded in the programming code. As As new knowledge becomes available, the program has to be new knowledge becomes available, the program has to be changed and recompiled.changed and recompiled.


Characteristics that Distinguish Knowledge-based Expert Characteristics that Distinguish Knowledge-based Expert Systems from Conventional Algorithmic Problem-solving Systems from Conventional Algorithmic Problem-solving

SystemsSystems


SystemsSystems


Heuristic Knowledge-based Expert System Approaches to Heuristic Knowledge-based Expert System Approaches to Problem Solving:Problem Solving:

Knowledge-based expert systems, on the other hand, collect Knowledge-based expert systems, on the other hand, collect many small fragments of human know-how for a specific many small fragments of human know-how for a specific application area (domain) and place them in a application area (domain) and place them in a knowledge baseknowledge base that is used to reason through a problem, using the knowledge that is used to reason through a problem, using the knowledge that is most appropriate. Characteristics that distinguish that is most appropriate. Characteristics that distinguish knowledge-based expert systems from conventional algorithmic knowledge-based expert systems from conventional algorithmic systems are summarized in the table. systems are summarized in the table. HeuristicHeuristic knowledgeknowledge is is defined as “involving or serving as an aid to learning, discovery, defined as “involving or serving as an aid to learning, discovery, or problem solving by experimental and especially by trial-and-or problem solving by experimental and especially by trial-and-error methods. Heuristic computer programs often utilize error methods. Heuristic computer programs often utilize exploratory problem-solving and self-educating techniques (as exploratory problem-solving and self-educating techniques (as the evaluation of feedback) to improve performance”.the evaluation of feedback) to improve performance”.

Heuristic Knowledge-based Expert System Approaches to Heuristic Knowledge-based Expert System Approaches to Problem Solving:Problem Solving:

Knowledge-based expert systems, on the other hand, collect Knowledge-based expert systems, on the other hand, collect many small fragments of human know-how for a specific many small fragments of human know-how for a specific application area (domain) and place them in a application area (domain) and place them in a knowledge baseknowledge base that is used to reason through a problem, using the knowledge that is used to reason through a problem, using the knowledge that is most appropriate. Characteristics that distinguish that is most appropriate. Characteristics that distinguish knowledge-based expert systems from conventional algorithmic knowledge-based expert systems from conventional algorithmic systems are summarized in the table. systems are summarized in the table. HeuristicHeuristic knowledgeknowledge is is defined as “involving or serving as an aid to learning, discovery, defined as “involving or serving as an aid to learning, discovery, or problem solving by experimental and especially by trial-and-or problem solving by experimental and especially by trial-and-error methods. Heuristic computer programs often utilize error methods. Heuristic computer programs often utilize exploratory problem-solving and self-educating techniques (as exploratory problem-solving and self-educating techniques (as the evaluation of feedback) to improve performance”.the evaluation of feedback) to improve performance”.



SystemsSystems


SystemsSystems



The Problem with ExpertsThe Problem with Experts

Unfortunately, most experts really do not know Unfortunately, most experts really do not know exactlyexactly how they how they perform their expert work. Much of their expertise is derived perform their expert work. Much of their expertise is derived from experiencing life and observing hundreds or even from experiencing life and observing hundreds or even thousands of case studies. It is difficult for the experts to thousands of case studies. It is difficult for the experts to understand the intricate workings of complex systems much less understand the intricate workings of complex systems much less be able to break them down into their constituent parts and then be able to break them down into their constituent parts and then mimic the decision-making process of the human mind. mimic the decision-making process of the human mind. Therefore, how does one get the knowledge embedded in the Therefore, how does one get the knowledge embedded in the mind of an expert into formal rules and conditions necessary to mind of an expert into formal rules and conditions necessary to create an expert system to solve relatively narrowly defined create an expert system to solve relatively narrowly defined hypotheses (problems)?hypotheses (problems)? This is the responsibility of the This is the responsibility of the knowledge engineerknowledge engineer..

The Problem with ExpertsThe Problem with Experts

Unfortunately, most experts really do not know Unfortunately, most experts really do not know exactlyexactly how they how they perform their expert work. Much of their expertise is derived perform their expert work. Much of their expertise is derived from experiencing life and observing hundreds or even from experiencing life and observing hundreds or even thousands of case studies. It is difficult for the experts to thousands of case studies. It is difficult for the experts to understand the intricate workings of complex systems much less understand the intricate workings of complex systems much less be able to break them down into their constituent parts and then be able to break them down into their constituent parts and then mimic the decision-making process of the human mind. mimic the decision-making process of the human mind. Therefore, how does one get the knowledge embedded in the Therefore, how does one get the knowledge embedded in the mind of an expert into formal rules and conditions necessary to mind of an expert into formal rules and conditions necessary to create an expert system to solve relatively narrowly defined create an expert system to solve relatively narrowly defined hypotheses (problems)?hypotheses (problems)? This is the responsibility of the This is the responsibility of the knowledge engineerknowledge engineer..



The The knowledge engineerknowledge engineer interrogates the interrogates the domain expertdomain expert and extracts and extracts as many rules and conditions as possible that are relevant to the as many rules and conditions as possible that are relevant to the hypotheses (problems) being examined. Ideally, the knowledge hypotheses (problems) being examined. Ideally, the knowledge engineer has unique capabilities that allow him or her to help engineer has unique capabilities that allow him or her to help build the most appropriate rules. This is not easy. The knowledge build the most appropriate rules. This is not easy. The knowledge engineering process can be costly and time-consuming. engineering process can be costly and time-consuming.

Recently, it has become acceptable for a Recently, it has become acceptable for a domain expertdomain expert (e.g., (e.g., biologist, geographer) to create his or her own knowledge-based biologist, geographer) to create his or her own knowledge-based expert system by querying oneself and hopefully accurately expert system by querying oneself and hopefully accurately specifying the rules associated with the problem at hand, for specifying the rules associated with the problem at hand, for example, using ERDAS Imagine’s expert system Knowledge example, using ERDAS Imagine’s expert system Knowledge Engineer. When this activity takes place, the expert must have a Engineer. When this activity takes place, the expert must have a wealth of knowledge in a certain domain and the ability to wealth of knowledge in a certain domain and the ability to formulate a hypothesis and parse the rules and conditions into formulate a hypothesis and parse the rules and conditions into understandable elements that are amenable to the “understandable elements that are amenable to the “knowledge knowledge representation processrepresentation process.”.”

The The knowledge engineerknowledge engineer interrogates the interrogates the domain expertdomain expert and extracts and extracts as many rules and conditions as possible that are relevant to the as many rules and conditions as possible that are relevant to the hypotheses (problems) being examined. Ideally, the knowledge hypotheses (problems) being examined. Ideally, the knowledge engineer has unique capabilities that allow him or her to help engineer has unique capabilities that allow him or her to help build the most appropriate rules. This is not easy. The knowledge build the most appropriate rules. This is not easy. The knowledge engineering process can be costly and time-consuming. engineering process can be costly and time-consuming.

Recently, it has become acceptable for a Recently, it has become acceptable for a domain expertdomain expert (e.g., (e.g., biologist, geographer) to create his or her own knowledge-based biologist, geographer) to create his or her own knowledge-based expert system by querying oneself and hopefully accurately expert system by querying oneself and hopefully accurately specifying the rules associated with the problem at hand, for specifying the rules associated with the problem at hand, for example, using ERDAS Imagine’s expert system Knowledge example, using ERDAS Imagine’s expert system Knowledge Engineer. When this activity takes place, the expert must have a Engineer. When this activity takes place, the expert must have a wealth of knowledge in a certain domain and the ability to wealth of knowledge in a certain domain and the ability to formulate a hypothesis and parse the rules and conditions into formulate a hypothesis and parse the rules and conditions into understandable elements that are amenable to the “understandable elements that are amenable to the “knowledge knowledge representation processrepresentation process.”.”


Knowledge Representation Process Knowledge Representation Process Knowledge Representation Process Knowledge Representation Process

The The knowledge representation processknowledge representation process normally involves encoding normally involves encoding information from verbal descriptions, rules of thumb, images, information from verbal descriptions, rules of thumb, images, books, maps, charts, tables, graphs, equations, etc. Hopefully, the books, maps, charts, tables, graphs, equations, etc. Hopefully, the knowledge base contains sufficient high-quality rules to solve knowledge base contains sufficient high-quality rules to solve the problem under investigation. the problem under investigation. RulesRules are normally expressed in are normally expressed in the form of one or more “the form of one or more “IF condition THEN actionIF condition THEN action” statements. ” statements. The The conditioncondition portion of a rule statement is usually a portion of a rule statement is usually a factfact, e.g., , e.g., the pixel under investigation must reflect > 45% of the incident the pixel under investigation must reflect > 45% of the incident near-infrared energy. When certain rules are applied, various near-infrared energy. When certain rules are applied, various operations may take place such as adding a newly derived operations may take place such as adding a newly derived derivative fact to the database or firing another rule. Rules can be derivative fact to the database or firing another rule. Rules can be implicit (slope is high) or explicit (e.g., slope > 70%). It is implicit (slope is high) or explicit (e.g., slope > 70%). It is possible to chain together rules, e.g., IF possible to chain together rules, e.g., IF cc THEN THEN dd; IF ; IF dd THEN THEN ee; therefore IF ; therefore IF cc THEN THEN ee. It is also possible to attach confidences . It is also possible to attach confidences (e.g., 80% confident) to facts and rules. (e.g., 80% confident) to facts and rules.

The The knowledge representation processknowledge representation process normally involves encoding normally involves encoding information from verbal descriptions, rules of thumb, images, information from verbal descriptions, rules of thumb, images, books, maps, charts, tables, graphs, equations, etc. Hopefully, the books, maps, charts, tables, graphs, equations, etc. Hopefully, the knowledge base contains sufficient high-quality rules to solve knowledge base contains sufficient high-quality rules to solve the problem under investigation. the problem under investigation. RulesRules are normally expressed in are normally expressed in the form of one or more “the form of one or more “IF condition THEN actionIF condition THEN action” statements. ” statements. The The conditioncondition portion of a rule statement is usually a portion of a rule statement is usually a factfact, e.g., , e.g., the pixel under investigation must reflect > 45% of the incident the pixel under investigation must reflect > 45% of the incident near-infrared energy. When certain rules are applied, various near-infrared energy. When certain rules are applied, various operations may take place such as adding a newly derived operations may take place such as adding a newly derived derivative fact to the database or firing another rule. Rules can be derivative fact to the database or firing another rule. Rules can be implicit (slope is high) or explicit (e.g., slope > 70%). It is implicit (slope is high) or explicit (e.g., slope > 70%). It is possible to chain together rules, e.g., IF possible to chain together rules, e.g., IF cc THEN THEN dd; IF ; IF dd THEN THEN ee; therefore IF ; therefore IF cc THEN THEN ee. It is also possible to attach confidences . It is also possible to attach confidences (e.g., 80% confident) to facts and rules. (e.g., 80% confident) to facts and rules.


For example, a typical rule used by the MYCIN expert system isFor example, a typical rule used by the MYCIN expert system is

IF the IF the stainstain of the organism is gram-negative of the organism is gram-negative AND the AND the morphologymorphology of the organism is rod of the organism is rod AND the AND the aerobicityaerobicity of the organism is anaerobic of the organism is anaerobic THEN there is strong suggestive evidence (0.8) that the THEN there is strong suggestive evidence (0.8) that the

class of the organism is class of the organism is Enterobacter iaceaeEnterobacter iaceae..

For example, a typical rule used by the MYCIN expert system isFor example, a typical rule used by the MYCIN expert system is

IF the IF the stainstain of the organism is gram-negative of the organism is gram-negative AND the AND the morphologymorphology of the organism is rod of the organism is rod AND the AND the aerobicityaerobicity of the organism is anaerobic of the organism is anaerobic THEN there is strong suggestive evidence (0.8) that the THEN there is strong suggestive evidence (0.8) that the

class of the organism is class of the organism is Enterobacter iaceaeEnterobacter iaceae..


Following the same format, a typical remote sensing rule might be: Following the same format, a typical remote sensing rule might be: IF IF blueblue reflectance is (Condition) < 15% reflectance is (Condition) < 15% AND AND greengreen reflectance is (Condition) < 25% reflectance is (Condition) < 25% AND AND redred reflectance is (Condition) < 15% reflectance is (Condition) < 15% AND AND near-infrarednear-infrared reflectance is (Condition) > 45% reflectance is (Condition) > 45% THEN there is strong suggestive evidence (0.8) that the THEN there is strong suggestive evidence (0.8) that the pixel is vegetated.pixel is vegetated.

Following the same format, a typical remote sensing rule might be: Following the same format, a typical remote sensing rule might be: IF IF blueblue reflectance is (Condition) < 15% reflectance is (Condition) < 15% AND AND greengreen reflectance is (Condition) < 25% reflectance is (Condition) < 25% AND AND redred reflectance is (Condition) < 15% reflectance is (Condition) < 15% AND AND near-infrarednear-infrared reflectance is (Condition) > 45% reflectance is (Condition) > 45% THEN there is strong suggestive evidence (0.8) that the THEN there is strong suggestive evidence (0.8) that the pixel is vegetated.pixel is vegetated.


Decision TreesDecision Trees The best way to conceptualize an expert system is to use a The best way to conceptualize an expert system is to use a

decision-tree structuredecision-tree structure where rules and conditions are evaluated where rules and conditions are evaluated in order to test hypotheses. When decision trees are organized in order to test hypotheses. When decision trees are organized with hypotheses, rules, and conditions, each hypothesis may be with hypotheses, rules, and conditions, each hypothesis may be thought of as the trunk of a tree, each rule a limb of a tree, and thought of as the trunk of a tree, each rule a limb of a tree, and each condition a leaf. This is commonly referred to as a each condition a leaf. This is commonly referred to as a hierarchical decision-tree classifierhierarchical decision-tree classifier (e.g., Swain and Hauska, (e.g., Swain and Hauska, 1977; Jensen, 1978; Kim and Landgrebe, 1991; DeFries and 1977; Jensen, 1978; Kim and Landgrebe, 1991; DeFries and Chan, 2000; Stow et al., 2003; Zhang and Wang, 2003). Chan, 2000; Stow et al., 2003; Zhang and Wang, 2003). The The purpose of using a purpose of using a hierarchical structurehierarchical structure for labeling objects is for labeling objects is to gain a more comprehensive understanding of relationships to gain a more comprehensive understanding of relationships among objects at different scales of observation or at different among objects at different scales of observation or at different levels of detail. levels of detail.

Decision TreesDecision Trees The best way to conceptualize an expert system is to use a The best way to conceptualize an expert system is to use a

decision-tree structuredecision-tree structure where rules and conditions are evaluated where rules and conditions are evaluated in order to test hypotheses. When decision trees are organized in order to test hypotheses. When decision trees are organized with hypotheses, rules, and conditions, each hypothesis may be with hypotheses, rules, and conditions, each hypothesis may be thought of as the trunk of a tree, each rule a limb of a tree, and thought of as the trunk of a tree, each rule a limb of a tree, and each condition a leaf. This is commonly referred to as a each condition a leaf. This is commonly referred to as a hierarchical decision-tree classifierhierarchical decision-tree classifier (e.g., Swain and Hauska, (e.g., Swain and Hauska, 1977; Jensen, 1978; Kim and Landgrebe, 1991; DeFries and 1977; Jensen, 1978; Kim and Landgrebe, 1991; DeFries and Chan, 2000; Stow et al., 2003; Zhang and Wang, 2003). Chan, 2000; Stow et al., 2003; Zhang and Wang, 2003). The The purpose of using a purpose of using a hierarchical structurehierarchical structure for labeling objects is for labeling objects is to gain a more comprehensive understanding of relationships to gain a more comprehensive understanding of relationships among objects at different scales of observation or at different among objects at different scales of observation or at different levels of detail. levels of detail.



Decision TreesDecision Trees

A decision tree takes as input an object or situation described by A decision tree takes as input an object or situation described by a set of attributes and returns a decision. The input attributes can a set of attributes and returns a decision. The input attributes can be discrete or continuous. The output value can also be discrete be discrete or continuous. The output value can also be discrete or continuous. Learning a or continuous. Learning a discrete-valued functiondiscrete-valued function is called is called classificationclassification learninglearning. Learning a . Learning a continuous functioncontinuous function is called is called regressionregression. We will concentrate on Boolean classification . We will concentrate on Boolean classification wherein each example is classified as true (positive) or false wherein each example is classified as true (positive) or false (negative). A decision tree reaches its decision by performing a (negative). A decision tree reaches its decision by performing a sequence of tests.sequence of tests.

Decision TreesDecision Trees

A decision tree takes as input an object or situation described by A decision tree takes as input an object or situation described by a set of attributes and returns a decision. The input attributes can a set of attributes and returns a decision. The input attributes can be discrete or continuous. The output value can also be discrete be discrete or continuous. The output value can also be discrete or continuous. Learning a or continuous. Learning a discrete-valued functiondiscrete-valued function is called is called classificationclassification learninglearning. Learning a . Learning a continuous functioncontinuous function is called is called regressionregression. We will concentrate on Boolean classification . We will concentrate on Boolean classification wherein each example is classified as true (positive) or false wherein each example is classified as true (positive) or false (negative). A decision tree reaches its decision by performing a (negative). A decision tree reaches its decision by performing a sequence of tests.sequence of tests.



Hypothesis 1:Hypothesis 1: the terrain (pixel) is suitable for residential development that the terrain (pixel) is suitable for residential development that

makes maximum use of solar energy (i.e., I will be able to put makes maximum use of solar energy (i.e., I will be able to put solar panels on my roof ).solar panels on my roof ).

Hypothesis 1:Hypothesis 1: the terrain (pixel) is suitable for residential development that the terrain (pixel) is suitable for residential development that

makes maximum use of solar energy (i.e., I will be able to put makes maximum use of solar energy (i.e., I will be able to put solar panels on my roof ).solar panels on my roof ).



Specify the expert system rulesSpecify the expert system rules: :

Heuristic rules that the expert has learned over time are the heart Heuristic rules that the expert has learned over time are the heart and soul of an expert system. If the expert’s heuristic rules of and soul of an expert system. If the expert’s heuristic rules of thumb are indeed based on correct principles, then the expert thumb are indeed based on correct principles, then the expert system will most likely function properly. If the expert does not system will most likely function properly. If the expert does not understand all the subtle nuances of the problem, has left out understand all the subtle nuances of the problem, has left out important variables or interaction among variables, or applied important variables or interaction among variables, or applied too much significance (weight) to certain variables, the expert too much significance (weight) to certain variables, the expert system outcome may not be accurate. Therefore, the creation of system outcome may not be accurate. Therefore, the creation of accurate, definitive rules is extremely important. Each rule accurate, definitive rules is extremely important. Each rule provides the specific conditions to accept the hypothesis to provides the specific conditions to accept the hypothesis to which it belongs. A single rule that might be associated with which it belongs. A single rule that might be associated with hypothesis 1 is:hypothesis 1 is:

specific combinations of terrain slope, aspect, and proximity to specific combinations of terrain slope, aspect, and proximity to shadows result in maximum exposure to sunlight.shadows result in maximum exposure to sunlight.

Specify the expert system rulesSpecify the expert system rules: :

Heuristic rules that the expert has learned over time are the heart Heuristic rules that the expert has learned over time are the heart and soul of an expert system. If the expert’s heuristic rules of and soul of an expert system. If the expert’s heuristic rules of thumb are indeed based on correct principles, then the expert thumb are indeed based on correct principles, then the expert system will most likely function properly. If the expert does not system will most likely function properly. If the expert does not understand all the subtle nuances of the problem, has left out understand all the subtle nuances of the problem, has left out important variables or interaction among variables, or applied important variables or interaction among variables, or applied too much significance (weight) to certain variables, the expert too much significance (weight) to certain variables, the expert system outcome may not be accurate. Therefore, the creation of system outcome may not be accurate. Therefore, the creation of accurate, definitive rules is extremely important. Each rule accurate, definitive rules is extremely important. Each rule provides the specific conditions to accept the hypothesis to provides the specific conditions to accept the hypothesis to which it belongs. A single rule that might be associated with which it belongs. A single rule that might be associated with hypothesis 1 is:hypothesis 1 is:

specific combinations of terrain slope, aspect, and proximity to specific combinations of terrain slope, aspect, and proximity to shadows result in maximum exposure to sunlight.shadows result in maximum exposure to sunlight.



Specify the rule conditionsSpecify the rule conditions::

The expert would then specify one or more The expert would then specify one or more conditionsconditions that must that must be met for each be met for each rulerule. For example, conditions for the rule stated . For example, conditions for the rule stated above might include:above might include:

• slopeslope > 0 degrees, AND > 0 degrees, AND• slopeslope < 10 degrees (i.e., the terrain should ideally lie on terrain < 10 degrees (i.e., the terrain should ideally lie on terrain with 1 to 9 degrees slope), ANDwith 1 to 9 degrees slope), AND• aspectaspect > 135 degrees, AND > 135 degrees, AND• aspectaspect < 220 degrees (i.e., in the Northern Hemisphere the < 220 degrees (i.e., in the Northern Hemisphere the terrain should ideally face south between 136 and 219 terrain should ideally face south between 136 and 219

degrees to obtain maximum exposure to sunlight), AND degrees to obtain maximum exposure to sunlight), AND • the terrain is not intersected by the terrain is not intersected by shadowsshadows cast by neighboring cast by neighboring terrain, trees, or other buildings (derived from a viewshed terrain, trees, or other buildings (derived from a viewshed

model).model).

Specify the rule conditionsSpecify the rule conditions::

The expert would then specify one or more The expert would then specify one or more conditionsconditions that must that must be met for each be met for each rulerule. For example, conditions for the rule stated . For example, conditions for the rule stated above might include:above might include:

• slopeslope > 0 degrees, AND > 0 degrees, AND• slopeslope < 10 degrees (i.e., the terrain should ideally lie on terrain < 10 degrees (i.e., the terrain should ideally lie on terrain with 1 to 9 degrees slope), ANDwith 1 to 9 degrees slope), AND• aspectaspect > 135 degrees, AND > 135 degrees, AND• aspectaspect < 220 degrees (i.e., in the Northern Hemisphere the < 220 degrees (i.e., in the Northern Hemisphere the terrain should ideally face south between 136 and 219 terrain should ideally face south between 136 and 219

degrees to obtain maximum exposure to sunlight), AND degrees to obtain maximum exposure to sunlight), AND • the terrain is not intersected by the terrain is not intersected by shadowsshadows cast by neighboring cast by neighboring terrain, trees, or other buildings (derived from a viewshed terrain, trees, or other buildings (derived from a viewshed

model).model). Jensen, 2005Jensen, 2005Jensen, 2005Jensen, 2005



A human-derived decision-tree expert system with a rule and conditions to A human-derived decision-tree expert system with a rule and conditions to be investigated by an inference engine to test be investigated by an inference engine to test Hypothesis 1:Hypothesis 1: the terrain the terrain (pixel) is suitable for residential development that makes maximum use of (pixel) is suitable for residential development that makes maximum use of solar energy (i.e., I will be able to put solar panels on my roof ).solar energy (i.e., I will be able to put solar panels on my roof ).

A human-derived decision-tree expert system with a rule and conditions to A human-derived decision-tree expert system with a rule and conditions to be investigated by an inference engine to test be investigated by an inference engine to test Hypothesis 1:Hypothesis 1: the terrain the terrain (pixel) is suitable for residential development that makes maximum use of (pixel) is suitable for residential development that makes maximum use of solar energy (i.e., I will be able to put solar panels on my roof ).solar energy (i.e., I will be able to put solar panels on my roof ).


Inference EngineInference EngineInference EngineInference Engine

The terms The terms reasoningreasoning and and inferenceinference are used to describe any process are used to describe any process by which conclusions are reached. by which conclusions are reached. Thus, the hypotheses, rules, Thus, the hypotheses, rules, and conditions are passed to the inference engine where the and conditions are passed to the inference engine where the expert system is implemented.expert system is implemented. One or more conditional One or more conditional statements within each rule are evaluated using the spatial data statements within each rule are evaluated using the spatial data (e.g., 135 < (e.g., 135 < aspectaspect < 220). Multiple conditions within a rule are < 220). Multiple conditions within a rule are evaluated based on Boolean AND logic. While all of the evaluated based on Boolean AND logic. While all of the conditions within a rule must be met to satisfy the rule, any conditions within a rule must be met to satisfy the rule, any single rule within a hypothesis can cause that hypothesis to be single rule within a hypothesis can cause that hypothesis to be accepted or rejected. In some cases, rules within a hypothesis accepted or rejected. In some cases, rules within a hypothesis disagree on the outcome and a decision must be made using rule disagree on the outcome and a decision must be made using rule confidences (e.g., a confidence of 0.8 in a preferred rule and a confidences (e.g., a confidence of 0.8 in a preferred rule and a confidence of 0.7 in another) or the order of the rules (e.g., confidence of 0.7 in another) or the order of the rules (e.g., preference given to the first) as the factor. The confidences and preference given to the first) as the factor. The confidences and order associated with the rules are stipulated by the expert.order associated with the rules are stipulated by the expert.

The terms The terms reasoningreasoning and and inferenceinference are used to describe any process are used to describe any process by which conclusions are reached. by which conclusions are reached. Thus, the hypotheses, rules, Thus, the hypotheses, rules, and conditions are passed to the inference engine where the and conditions are passed to the inference engine where the expert system is implemented.expert system is implemented. One or more conditional One or more conditional statements within each rule are evaluated using the spatial data statements within each rule are evaluated using the spatial data (e.g., 135 < (e.g., 135 < aspectaspect < 220). Multiple conditions within a rule are < 220). Multiple conditions within a rule are evaluated based on Boolean AND logic. While all of the evaluated based on Boolean AND logic. While all of the conditions within a rule must be met to satisfy the rule, any conditions within a rule must be met to satisfy the rule, any single rule within a hypothesis can cause that hypothesis to be single rule within a hypothesis can cause that hypothesis to be accepted or rejected. In some cases, rules within a hypothesis accepted or rejected. In some cases, rules within a hypothesis disagree on the outcome and a decision must be made using rule disagree on the outcome and a decision must be made using rule confidences (e.g., a confidence of 0.8 in a preferred rule and a confidences (e.g., a confidence of 0.8 in a preferred rule and a confidence of 0.7 in another) or the order of the rules (e.g., confidence of 0.7 in another) or the order of the rules (e.g., preference given to the first) as the factor. The confidences and preference given to the first) as the factor. The confidences and order associated with the rules are stipulated by the expert.order associated with the rules are stipulated by the expert.



The inference engine interprets the rules in the knowledge base The inference engine interprets the rules in the knowledge base to draw conclusions.to draw conclusions. The inference engine may use backward- or The inference engine may use backward- or forward-chaining strategies or both. Both backward and forward forward-chaining strategies or both. Both backward and forward inference processes consist of a chain of steps that can be traced inference processes consist of a chain of steps that can be traced by the expert system. This enables expert systems to explain by the expert system. This enables expert systems to explain their reasoning processes, which is an important and positive their reasoning processes, which is an important and positive characteristic of expert systems. characteristic of expert systems. You would expect a doctor to You would expect a doctor to explain how he or she came to a certain diagnosis regarding explain how he or she came to a certain diagnosis regarding your health.your health. An expert system can provide explicit information An expert system can provide explicit information about how a particular conclusion (diagnosis) was reached.about how a particular conclusion (diagnosis) was reached.

The inference engine interprets the rules in the knowledge base The inference engine interprets the rules in the knowledge base to draw conclusions.to draw conclusions. The inference engine may use backward- or The inference engine may use backward- or forward-chaining strategies or both. Both backward and forward forward-chaining strategies or both. Both backward and forward inference processes consist of a chain of steps that can be traced inference processes consist of a chain of steps that can be traced by the expert system. This enables expert systems to explain by the expert system. This enables expert systems to explain their reasoning processes, which is an important and positive their reasoning processes, which is an important and positive characteristic of expert systems. characteristic of expert systems. You would expect a doctor to You would expect a doctor to explain how he or she came to a certain diagnosis regarding explain how he or she came to a certain diagnosis regarding your health.your health. An expert system can provide explicit information An expert system can provide explicit information about how a particular conclusion (diagnosis) was reached.about how a particular conclusion (diagnosis) was reached.



An An expert system expert system shellshell provides a customizable inference engine. provides a customizable inference engine. Expert system shells come equipped with an inference Expert system shells come equipped with an inference mechanism (backward chaining, forward chaining, or both) and mechanism (backward chaining, forward chaining, or both) and require knowledge to be entered according to a specified format. require knowledge to be entered according to a specified format. Expert system shells qualify as languages, although with a Expert system shells qualify as languages, although with a narrower range of application than most programming narrower range of application than most programming languages. Typical artificial intelligence programming languages languages. Typical artificial intelligence programming languages include LISP, developed in the 1950s, PROLOG, developed in include LISP, developed in the 1950s, PROLOG, developed in the 1970s, and now object-oriented languages such as Cthe 1970s, and now object-oriented languages such as C++++..

An An expert system expert system shellshell provides a customizable inference engine. provides a customizable inference engine. Expert system shells come equipped with an inference Expert system shells come equipped with an inference mechanism (backward chaining, forward chaining, or both) and mechanism (backward chaining, forward chaining, or both) and require knowledge to be entered according to a specified format. require knowledge to be entered according to a specified format. Expert system shells qualify as languages, although with a Expert system shells qualify as languages, although with a narrower range of application than most programming narrower range of application than most programming languages. Typical artificial intelligence programming languages languages. Typical artificial intelligence programming languages include LISP, developed in the 1950s, PROLOG, developed in include LISP, developed in the 1950s, PROLOG, developed in the 1970s, and now object-oriented languages such as Cthe 1970s, and now object-oriented languages such as C++++..


Expert Systems Applied to Remote Sensor DataExpert Systems Applied to Remote Sensor Data

The use of expert systems in remote sensing research will be The use of expert systems in remote sensing research will be demonstrated using two different methodologies used to create demonstrated using two different methodologies used to create the rules and conditions in the knowledge base. The first expert the rules and conditions in the knowledge base. The first expert system classification is based on the use of system classification is based on the use of formal rules formal rules developed by a human expertdeveloped by a human expert. The second example involves . The second example involves expert system rules derived automatically by an expert system rules derived automatically by an inductiveinductive machine-learning algorithmmachine-learning algorithm based on training data that is input based on training data that is input by humans into the system. Both methods are used to identify by humans into the system. Both methods are used to identify white fir forest stands on Maple Mountain in Utah County, Utah, white fir forest stands on Maple Mountain in Utah County, Utah, using Landsat Enhanced Thematic Mapper Plus (ETMusing Landsat Enhanced Thematic Mapper Plus (ETM++) imagery ) imagery and topographic variables extracted from a digital elevation and topographic variables extracted from a digital elevation model of the area. model of the area.

The use of expert systems in remote sensing research will be The use of expert systems in remote sensing research will be demonstrated using two different methodologies used to create demonstrated using two different methodologies used to create the rules and conditions in the knowledge base. The first expert the rules and conditions in the knowledge base. The first expert system classification is based on the use of system classification is based on the use of formal rules formal rules developed by a human expertdeveloped by a human expert. The second example involves . The second example involves expert system rules derived automatically by an expert system rules derived automatically by an inductiveinductive machine-learning algorithmmachine-learning algorithm based on training data that is input based on training data that is input by humans into the system. Both methods are used to identify by humans into the system. Both methods are used to identify white fir forest stands on Maple Mountain in Utah County, Utah, white fir forest stands on Maple Mountain in Utah County, Utah, using Landsat Enhanced Thematic Mapper Plus (ETMusing Landsat Enhanced Thematic Mapper Plus (ETM++) imagery ) imagery and topographic variables extracted from a digital elevation and topographic variables extracted from a digital elevation model of the area. model of the area.


Expert System Applied to Remote Sensor DataExpert System Applied to Remote Sensor Data

A hypothesis (class), variables, and conditions necessary to extract A hypothesis (class), variables, and conditions necessary to extract white firwhite fir ((AbiesAbies concolorconcolor) forest cover information from Maple Mountain, Utah, using ) forest cover information from Maple Mountain, Utah, using remote sensing and digital elevation model data. The Boolean logic with which remote sensing and digital elevation model data. The Boolean logic with which these variables and conditions are organized within a chain of inference may be these variables and conditions are organized within a chain of inference may be controlled by the use of rules and sub-hypotheses.controlled by the use of rules and sub-hypotheses.

A hypothesis (class), variables, and conditions necessary to extract A hypothesis (class), variables, and conditions necessary to extract white firwhite fir ((AbiesAbies concolorconcolor) forest cover information from Maple Mountain, Utah, using ) forest cover information from Maple Mountain, Utah, using remote sensing and digital elevation model data. The Boolean logic with which remote sensing and digital elevation model data. The Boolean logic with which these variables and conditions are organized within a chain of inference may be these variables and conditions are organized within a chain of inference may be controlled by the use of rules and sub-hypotheses.controlled by the use of rules and sub-hypotheses.


Classification of White Fir on Maple Mountain, Classification of White Fir on Maple Mountain, Utah County using Utah County using HierarchicalHierarchical Decision Tree LogicDecision Tree Logic

Classification of White Fir on Maple Mountain, Classification of White Fir on Maple Mountain, Utah County using Utah County using HierarchicalHierarchical Decision Tree LogicDecision Tree Logic

1 × 1 m NAPP aerial photography (acquired 17 Aug 1994) 1 × 1 m NAPP aerial photography (acquired 17 Aug 1994) is draped over a 10 × 10 m USGS DEM.is draped over a 10 × 10 m USGS DEM.

1 × 1 m NAPP aerial photography (acquired 17 Aug 1994) 1 × 1 m NAPP aerial photography (acquired 17 Aug 1994) is draped over a 10 × 10 m USGS DEM.is draped over a 10 × 10 m USGS DEM.Jensen, 2005Jensen, 2005

30 × 30 USGS DEM30 × 30 USGS DEM30 × 30 USGS DEM30 × 30 USGS DEM Shaded ReliefShaded ReliefShaded ReliefShaded Relief ContoursContoursContoursContours SlopeSlopeSlopeSlope AspectAspectAspectAspect

ETM PanchromaticETM PanchromaticETM PanchromaticETM Panchromatic ETM RGB = 5,4,2ETM RGB = 5,4,2ETM RGB = 5,4,2ETM RGB = 5,4,2 ETM RGB = 4,3,2ETM RGB = 4,3,2ETM RGB = 4,3,2ETM RGB = 4,3,2 ETM NDVIETM NDVIETM NDVIETM NDVI


Terrestrial PhotographTerrestrial PhotographTerrestrial PhotographTerrestrial Photograph

ETM RGB = 5,4,2ETM RGB = 5,4,2ETM RGB = 5,4,2ETM RGB = 5,4,2

ETM RGB = 4,3,2ETM RGB = 4,3,2ETM RGB = 4,3,2ETM RGB = 4,3,2

ETM PanchromaticETM PanchromaticETM PanchromaticETM Panchromatic

Expert’s Classification of White FirExpert’s Classification of White FirExpert’s Classification of White FirExpert’s Classification of White Fir



Predicted White FirPredicted White FirPredicted White FirPredicted White Fir

Expert’s ModelExpert’s ModelExpert’s ModelExpert’s Model

Hierarchical Decision Tree ClassifierHierarchical Decision Tree ClassifierHierarchical Decision Tree ClassifierHierarchical Decision Tree Classifier


Rules and Conditions Based on Machine LearningRules and Conditions Based on Machine LearningRules and Conditions Based on Machine LearningRules and Conditions Based on Machine Learning

The heart of an expert system is its The heart of an expert system is its knowledge baseknowledge base. The usual . The usual method of acquiring knowledge in a computer-usable format to method of acquiring knowledge in a computer-usable format to build a knowledge base involves human domain experts and build a knowledge base involves human domain experts and knowledge engineers, as previously discussed. The human domain knowledge engineers, as previously discussed. The human domain expert explicitly expresses his or her knowledge about a subject in expert explicitly expresses his or her knowledge about a subject in a language that can be understood by the knowledge engineer. The a language that can be understood by the knowledge engineer. The knowledge engineer translates the domain knowledge into a knowledge engineer translates the domain knowledge into a computer-usable format and stores it in the knowledge base.computer-usable format and stores it in the knowledge base.

The heart of an expert system is its The heart of an expert system is its knowledge baseknowledge base. The usual . The usual method of acquiring knowledge in a computer-usable format to method of acquiring knowledge in a computer-usable format to build a knowledge base involves human domain experts and build a knowledge base involves human domain experts and knowledge engineers, as previously discussed. The human domain knowledge engineers, as previously discussed. The human domain expert explicitly expresses his or her knowledge about a subject in expert explicitly expresses his or her knowledge about a subject in a language that can be understood by the knowledge engineer. The a language that can be understood by the knowledge engineer. The knowledge engineer translates the domain knowledge into a knowledge engineer translates the domain knowledge into a computer-usable format and stores it in the knowledge base.computer-usable format and stores it in the knowledge base.



This process presents a well-known problem in creating expert systems that is This process presents a well-known problem in creating expert systems that is often referred to as the often referred to as the knowledge acquisition bottleneckknowledge acquisition bottleneck.. The reasons are: The reasons are:

• the process requires the engagement of the domain expert and/or knowledge the process requires the engagement of the domain expert and/or knowledge engineer over a long period of time, and engineer over a long period of time, and • although experts are capable of using their knowledge for decisionmaking, they although experts are capable of using their knowledge for decisionmaking, they

are often incapable of articulating their knowledge explicitly in a format that is are often incapable of articulating their knowledge explicitly in a format that is sufficiently systematic, correct, and complete to be used in a computer sufficiently systematic, correct, and complete to be used in a computer application. application.

This process presents a well-known problem in creating expert systems that is This process presents a well-known problem in creating expert systems that is often referred to as the often referred to as the knowledge acquisition bottleneckknowledge acquisition bottleneck.. The reasons are: The reasons are:

• the process requires the engagement of the domain expert and/or knowledge the process requires the engagement of the domain expert and/or knowledge engineer over a long period of time, and engineer over a long period of time, and • although experts are capable of using their knowledge for decisionmaking, they although experts are capable of using their knowledge for decisionmaking, they

are often incapable of articulating their knowledge explicitly in a format that is are often incapable of articulating their knowledge explicitly in a format that is sufficiently systematic, correct, and complete to be used in a computer sufficiently systematic, correct, and complete to be used in a computer application. application.



To solve such problems, much effort has been exerted in the artificial intelligence To solve such problems, much effort has been exerted in the artificial intelligence community to automate the building of expert system knowledge bases.community to automate the building of expert system knowledge bases.

Machine learningMachine learning is defined as is defined as

““the science of computer modeling of learning processes”.the science of computer modeling of learning processes”.

It enables a computer to acquire knowledge from existing data or theories using It enables a computer to acquire knowledge from existing data or theories using certain inference strategies such as induction or deduction. We will focus only certain inference strategies such as induction or deduction. We will focus only on inductive learning and its application in building knowledge bases for image on inductive learning and its application in building knowledge bases for image analysis expert systems.analysis expert systems.

To solve such problems, much effort has been exerted in the artificial intelligence To solve such problems, much effort has been exerted in the artificial intelligence community to automate the building of expert system knowledge bases.community to automate the building of expert system knowledge bases.

Machine learningMachine learning is defined as is defined as

““the science of computer modeling of learning processes”.the science of computer modeling of learning processes”.

It enables a computer to acquire knowledge from existing data or theories using It enables a computer to acquire knowledge from existing data or theories using certain inference strategies such as induction or deduction. We will focus only certain inference strategies such as induction or deduction. We will focus only on inductive learning and its application in building knowledge bases for image on inductive learning and its application in building knowledge bases for image analysis expert systems.analysis expert systems.



A human being has the ability to make accurate generalizations A human being has the ability to make accurate generalizations from a few scattered facts provided by a teacher or the from a few scattered facts provided by a teacher or the environment using inductive inferences. This is called environment using inductive inferences. This is called inductive inductive learninglearning (Huang and Jensen, 1997). In machine learning, the (Huang and Jensen, 1997). In machine learning, the process of process of inductive learning can be viewed as a heuristic search inductive learning can be viewed as a heuristic search through a space of symbolic descriptions for plausible general through a space of symbolic descriptions for plausible general descriptions, or concepts, that explain the input training data and descriptions, or concepts, that explain the input training data and are useful for predicting new data.are useful for predicting new data. Inductive learning can be Inductive learning can be formulated using the following symbolic formulas.formulated using the following symbolic formulas.

A human being has the ability to make accurate generalizations A human being has the ability to make accurate generalizations from a few scattered facts provided by a teacher or the from a few scattered facts provided by a teacher or the environment using inductive inferences. This is called environment using inductive inferences. This is called inductive inductive learninglearning (Huang and Jensen, 1997). In machine learning, the (Huang and Jensen, 1997). In machine learning, the process of process of inductive learning can be viewed as a heuristic search inductive learning can be viewed as a heuristic search through a space of symbolic descriptions for plausible general through a space of symbolic descriptions for plausible general descriptions, or concepts, that explain the input training data and descriptions, or concepts, that explain the input training data and are useful for predicting new data.are useful for predicting new data. Inductive learning can be Inductive learning can be formulated using the following symbolic formulas.formulated using the following symbolic formulas.



C5.0 ModelC5.0 ModelC5.0 ModelC5.0 Model

Predicted White FirPredicted White FirPredicted White FirPredicted White Fir

Hierarchical Decision Tree Hierarchical Decision Tree Classifier Based on Inductive Classifier Based on Inductive Machine Learning Production Machine Learning Production

RulesRules

Hierarchical Decision Tree Hierarchical Decision Tree Classifier Based on Inductive Classifier Based on Inductive Machine Learning Production Machine Learning Production

RulesRules


Machine Learning-derived Classification MapMachine Learning-derived Classification MapMachine Learning-derived Classification MapMachine Learning-derived Classification Map



The following topics are covered in The following topics are covered in Geography 751:Geography 751: Seminar in Seminar in Remote SensingRemote Sensing::

- - Machine LearningMachine Learning

- - Neural NetworksNeural Networks

The following topics are covered in The following topics are covered in Geography 751:Geography 751: Seminar in Seminar in Remote SensingRemote Sensing::

- - Machine LearningMachine Learning

- - Neural NetworksNeural Networks


Documents

Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC 29208