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:: Information Fusion in Cartographic Feature Extraction from Aerial Imagery. David M. McKeown, Frederic P. Perlant, Jefferey Shufelt

Document type:: Multivolume work

Structure type:: Chapter

Information Fusion in Cartographic Feature Extraction from Aerial Imagery David M. McKeown Frederic P. Perlant Jefferey Shufelt Digital Mapping Laboratory School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract The extraction of buildings from aerial imagery is a complex problem for automated computer vision. It requires locating regions in a scene that possess properties distinguishing them as man-made objects and opposed to naturally occurring terrain features. The building extraction process requires techniques that exploit knowledge about the structure of man-made objects. Techniques do exist that take advantage of this knowledge; various methods use edge-line analysis, shadow analysis, and stereo imagery analysis to produce building hypotheses. It is reasonable, however, to assume that no single detection method will correctly delineate or verify buildings in every scene. As an example, imagine a feature extraction system that relied on analysis of cast shadows to predict building locations in cases where the sun was directly above the scene. It seems clear that a cooperative-methods paradigm is useful in approaching the building extraction problem. Using this paradigm, each extraction technique provides information which can then be added or assimilated into an overall interpretation of the scene. Thus, our research focus is to explore the development of a computer vision system that integrates the results of various scene analysis techniques into an accurate and robust interpretation of the underlying three-dimensional scene. This paper briefly describes research results in two areas. First, we describe the problem of building hypothesis fusion using only monocular cues in aerial imagery. Several building extraction techniques are briefly surveyed, including four building extraction, verification, and clustering systems that form the basis for the work described here. A method for fusing the symbolic data generated by these systems is described, and applied to monocular image and stereo image data sets. Evaluation methods for the fusion results are described, and the fusion results are analyzed using these methods. The second research area examines how estimates of three- dimensional scene structure, as encoded in a scene disparity map, can be improved by the analysis of the original monocular imagery. In some sense this procedure is counter intuitive. Since we have already used the imagery to perform stereo matching, what information could be available in either of the single images that would improve on the stereo analysis? We describe the utilization of surface illumination information provided by the segmentation of the monocular image into fine surface patches of nearly homogeneous intensity to remove mismatches generated during stereo matching. Such patches are used to guide a statistical analysis of the disparity map based on the assumption that such patches correspond closely with physical surfaces in the scene. This technique is quite independent of whether the initial disparity map was generated by automated area-based or feature-based stereo matching. 1 Introduction The extraction of significant man-made structures such as buildings and roads from aerial imagery is a complex problem that must be addressed in order to produce a fully automated cartographic feature extraction system. We focus on the building extraction process since buildings are present in almost all sites of cartographic interest and their robust detection and delineation requires techniques that exploit knowledge about man-made structures. There exist a multitude of techniques that take advantage of such knowledge; various methods use edge-line analysis, shadow analysis, stereo disparity analysis, and structural analysis to generate building hypotheses 1 ’ 2> 3 > 4,5< 6> 7 > 8 . It is reasonable, however, to assume that no single building extraction technique will perfectly delineate man-made structures in every scene. Consider the use of an edge- analysis method on an image where the ground intensity is similar to the intensity of the roofs of the buildings in the scene. As another example, consider the use of a shadow analysis method on an image in which the sun was directly above the scene. Clearly, a cooperative-methods paradigm is useful in approaching the building extraction problem. In this paradigm, it is assumed that no single method can provide a completely accurate or complete set of building hypotheses for a scene; each method can, however, provide a subset of the information necessary to produce an improved interpretation of building structure in the scene. For instance, a shadow-based method can provide useful information in situations where ground and roof intensity are similar; an edge-line analysis method can provide disambiguating information in cases where shadows were weak or nonexistent, or in situations where structures were sufficiently short that disparity analysis would not provide useful information. The implicit assumption of this paradigm is that the information produced by each detection technique can be integrated into a more meaningful collection of building hypotheses. Stereo matching provides a direct measurement of building location and height. In complex urban scenes, stereo matching based upon feature matching, i.e., edges, lines, and contours, appears to provide more accurate and robust matching than area-based techniques. This is primarily due to the ability of feature-based approaches to detect large depth discontinuities found in urban scenes. However, feature-based techniques generally provide only a sparse set of match points from which a three-dimensional surface is usually interpolated. In Section 3 we describe a method to integrate monocular surface intensity information with the stereo disparity map to refine the height estimates and reduce the effect of stereo matching errors. 140

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