Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Multivolume work

Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WA-1 KNOWLEDGE-BASED TECHNIQUES/ SYSTEMS FOR DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: MODEL-BASED ASSISTANCE FOR ANALYZING REMOTE SENSOR DATA. Wolf-Fritz Riekert, Thomas Ruwwe, Günther Hess

Document type:: Multivolume work

Structure type:: Chapter

MODEL-BASED ASSISTANCE FOR ANALYZING REMOTE SENSOR DATA Wolf-Fritz Riekert (Siemens, Munich and FAW, Ulm, West Germany) Thomas Ruwwe, Günter Hess (FAW, Ulm, West Germany) Bitnet: RIEKERT@DULFAW1A, RUWWE@DULFAW1A, HESS@DULFAW1A ABSTRACT The global objective of the RESEDA (REmote SEnsor Data Analysis) project, being conducted at the FAW Research Institute for Applied Knowledge Processing in Ulm (West Germany), is to make remote sensing technology available to a larger group of users in environmental management.* For this purpose, a knowledge-based advisory system is being developed, called the RESEDA Assistant. Processing models represented in a knowledge base are available to the RESEDA Assistant, which is thus able to facilitate the use of software tools for image processing or handling of spatial data. KEY WORDS: remote sensor data analysis, advisory system, knowledge-based, processing model 1. REMOTE SENSING AS A KNOWLEDGE- BASED ANALYSIS TASK Remote sensing produces a large amount of data that is relevant to the state of the environment. Analyzing this data requires both a great deal of computational power and a high degree of knowledge. Although the first requirement may be met by using conventional hardware and software, the second is very demanding on the experts working in this field. Because the number of qualified experts is small, there is a need for automated techniques that make remote sensing technology availa ble to a wider community of users. The goal of remote sensing in environmental protection is always to derive a certain piece of geographic informa tion. Moreover, the remote sensing data to be analyzed may itself be considered as a kind of geographic informa tion. That is, the task of processing remote sensing data is a typical task of transforming and analyzing geographic information. Remote sensing data as well as ancillary spatial and factual data describing geographic entities are input into the analysis process. The output of the analysis consists of environmental data related to the geographic entities to be analyzed. The analysis is controlled by the expert’s knowledge about concepts and methods of remo te sensing, image processing, and the geo-sciences. In RESEDA, we are trying to represent these concepts and methods in the knowledge base of an expert system (figure 1) (Riekert, 1990). Human experts in remote sensing are able to find a computational pathway from the source data to the target data. They know how to apply an appropriate sequence of image processing methods and manipulations of spatial data. This ability is based on two kinds of structural knowledge: Knowledge about remote sensing targets and their features, which are to be analyzed and computed in the course of the analysis. Knowledge about dependencies between these fea tures, implicating algorithms suited to compute cer tain features from one another. In RESEDA, an object-oriented formalism is used to describe this structural knowledge: - The various remote sensing targets and their features are represented by abstract target classes and target attributes. The concrete manifestations of these two concepts appear in the form of geographic data-, these are called classifications and attributions. The dependencies between features and the algo rithms to compute them are represented in an object- oriented form by abstract processing models and concrete computations of geographic data. In addition to the object-oriented representation techni que, rules are used to describe the conditions under which a processing model is adequate to compute certain geo graphic data and what the constraints are between the input and the output data of such a computation. * The RESEDA project is supported by the Federal State of Baden- Württemberg and by Siemens, Munich 146

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