Author:
Peter Hofmann
Affiliation:
Institute of Applied Informatics, Deggendorf Institute of Technology, Technology Campus Freyung, Grafenauer Str. 22, D-94078 Freyung, Germany Interfaculty Department of Geoinformatics, Z_GIS, Schillerstr. 30, A-5020 Salzburg and Austria
Keyword(s):
Multi-agent Systems, Remote Sensing, Object based and Agent based Image Analysis.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cognitive Systems
;
Computational Intelligence
;
Cooperation and Coordination
;
Data Manipulation
;
Distributed and Mobile Software Systems
;
Distributed Problem Solving
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Enterprise Ontologies
;
Evolutionary Computing
;
Formal Methods
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Model-Based Reasoning
;
Multi-Agent Systems
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Ontologies
;
Pattern Recognition
;
Physiological Computing Systems
;
Robot and Multi-Robot Systems
;
Sensor Networks
;
Simulation and Modeling
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
;
Uncertainty in AI
;
Vision and Perception
Abstract:
With remote sensing data and methods we gain deeper insight in many processes at the Earth’s surface. Thus, they are a valuable data source to gather geo-information of almost any kind. While the progress of remote sensing technology continues, the amount of available remote sensing data increases. Hence, besides effective strategies for data mining and image data retrieval, reliable and efficient methods of image analysis with a high degree of automation are needed in order to extract the information hidden in remote sensing data. Due to the complex nature of remote sensing data, recent methods of computer vision and image analysis do not allow a fully automatic and highly reliable analysis of remote sensing data, yet. Most of these methods are rather semi-automatic with a varying degree of automation depending on the data quality, the complexity of the image content and the information to be extracted. Thus, visual image interpretation in many cases is still seen as the most approp
riate method to gather (geo-) information from remote sensing data. To increase the degree of automation, the application of multi-agent systems in remote sensing image analysis is recently under research. The paper present summarizes recent approaches and outlines their potentials.
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