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:: A RULE-BASED SYSTEM FOR THE EXTRACTION OF CARTOGRAPHIC FEATURES FROM LANDSAT TM IMAGERY. M. Stadelmann, G. D. Lodwick

Document type:: Multivolume work

Structure type:: Chapter

161 A RULE-BASED SYSTEM FOR THE EXTRACTION OF CARTOGRAPHIC FEATURES FROM LANDSAT TM IMAGERY M. Stadelmann G.D. Lodwick Department of Surveying Engineering The University of Calgary 2500 University Drive N.W. Calgary, Alberta Canada T2N 1N4 Prepared for ISPRS COMMISSION VII Mid-term Symposium, Victoria, B.C., Canada, September 1990. ABSTRACT This paper describes work that has led to the development of a basic rule based system for the automated recognition of cartographic features in digital satellite imagery. The system uses frames to represent knowledge and is written in Arity PROLOG. Input to the rule based system consists of a segmented image modelled as a polygonal database. Image segmentation is carried out on the multispectral data using a migrating-means clustering algorithm. Simple landuse features, such as fields and water bodies, are extracted using spatial information, such as object areas, and simple shape measures, such as compactness ratios, combined with spectral knowledge. For more complex features, contextual information, such as adjacency, and more complex shape measures, such as elongation, are extracted from the image database. Results have been compared with conventional image interpretation and show acceptable accuracy, provided image segmentation errors are moderate. These conditions are met in images depicting rural areas as they contain spectrally homogeneous features. In more variable environments, such as urban areas, more advanced measures of coping with segmentation problems and the use of ancillary information are expected to generally enhance the success of automated image interpretation. KEY WORDS: Automated, digital, interpretation, mapping, cartography, Landsat, remote-sensing, GIS. 1 INTRODUCTION Earth observation satellites, such as the Landsat series or more recently SPOT, have the potential to facilitate map production and revision, as well as aid in the management of natural resources. They are particularly useful because their high resolutions and repetitive coverage permit the detection and monitoring of temporal changes on a regional and local scale. However, the increasing resolution poses the problem of increasingly large data volumes, which must be analyzed and managed (Maslanik and Smith, 1984). Electro-optical sensors capture data in digital format suitable for computer processing. However, automated feature extraction has mainly been restricted to statistical classification methods, which distinguish between surface cover classes on the basis of spectral signatures. The interpretation of the images, i.e. the assignment of meaningful names to cover classes, has remained a manual task, because image interpretation, in addition to spectral knowledge, requires a considerable amount of contextual information. Extraction of this type of information requires human expertise and decision making which cannot readily be accomplished using traditional algorithmic methods. In the conventional map production process, image interpreters typically analyze a satellite image and transfer any objects of interest manually onto a map. A GIS data base is then usually produced by digitizing these map products, which have already undergone abstraction and generalization compared to the original remote sensing data (McKeown, 1987). GIS products therefore usually result from one or more thematic map overlays. Image interpretation is a slow process which requires highly skilled personnel. Digitization and integration into the GIS data base is tedious and error prone. For example, if an image polygon is to be placed in the GIS, data base corruption will occur if there is no perfect juxtaposition of the image polygon and neighbouring map polygons (Goodenough et al., 1987). In addition, the accuracy of a finished map product decreases considerably if two or more digitized map overlays are utilized in its production (Newcomer and Szajgin, 1984). The time involved in image interpretation and quality control of map products in GIS has created a bottleneck in spatial data handling that has to be eliminated if resources managers and planners wish to make efficient use of the technology available to them.

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