Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

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

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:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 1 OVERVIEW OF IMAGE / DATA / INFORMATION FUSION AND INTEGRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INTEGRATION OF IMAGE ANALYSIS AND GIS. Emmanuel Baltsavias, Michael Hahn,

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 INTEGRATION OF IMAGE ANALYSIS AND GIS Emmanuel Baltsavias 1 , Michael Hahn 2 , 1 Institute of Geodesy and Photogrammetry, Swiss Federal Institute of Technology, ETH-Hoenggerberg, CH-8093 Zurich, Switzerland, Tel./Fax +41-1-633 3042 / 633 1101, manos@geod.ethz.ch 2 Faculty of Surveying and Geoinformatics, Stuttgart University of Applied Sciences, Schellingstr. 24, D-70174 Stuttgart, Germany, Tel./Fax +49-711-121 2712 / 121 2711, m.hahn.fbv@fht-stuttgart.de KEYWORDS: Integration, Fusion, Data Updating, Image Analysis, Interpretation, Reconstruction, GIS databases ABSTRACT Photogrammetry and remote sensing have proven their efficiency for spatial data collection in many ways. Interactive mapping at digital workstations is performed by skilled operators, which guarantees excellent quality in particular of the geometric data. In this way, worldwide acquisition of a large number of national GIS databases has been supported and still a lot of production effort is devoted to this task. In the field of image analysis, it has become evident that algorithms for scene interpretation and 3D reconstruction of topographic objects, which rely on a single data source, cannot function efficiently. Research in two directions promises to be more successful. Multiple largely complementary sensor data like range data from laser scanners or SAR and panchromatic or multispectral aerial images have been used to achieve robustness and better performance in image analysis. On the other hand, given GIS databases, e.g. layers from topographic maps, can be considered as virtual sensor data which contain geometric information together with its explicitly given semantics. In this case, image analysis aims at supplementing missing information, e.g. the extraction of the third dimension for 2D databases. A second goal, which is expected to become more important in future, is the revision and update of existing GIS databases. In this paper, we review recent developments in the overlapping area of image analysis and GIS. On the data side, we focus on different sensor data in conjunction with GIS databases. Analysis of these data addresses almost all aspects of knowledge based image analysis like detection, localisation, reconstruction and identification. The paper will focus on use of GIS databases to support image analysis and the opposite, as well as fusion of multiple cues from cooperative algorithms for object extraction, reconstruction and classification. Conceptual aspects behind new developments will also be described. In general, processes exploiting different information sources often have a lower algorithmic complexity compared with single sensor data processing. This, for example, was shown with building reconstruction based on range data and a given ground plan of the buildings. Another example is map update using spectral and spatial resolution satellite images. Given a topographic map supervised classification can be executed with the result that inconsistencies between map information and image information regarding geometry and semantics can be detected and localised. With this review we aim at summarising work of our InterCommission Working Group IV/HI.2 having in mind to promote further activities in this exciting field. 1. INTRODUCTION Before proceeding, some explanations on the term "Integration of Image Analysis and GIS" will be given. The word "integration" has been often used in relation to GIS, e.g. integration of Remote Sensing or of DTMs with GIS. By integration, we do not mean "concatenation" as mentioned in the definitions of an EARSeL-related working group (Wald, 1999). Integration, as in the term "system integration", means that different components are put together; these components co-operate which each other and lead to a better result or a result that could not have been achieved without this integration. In this sense, our definition of integration is similar to the definition of fusion given by the above-mentioned working group, although by fusion we understand something more restricted than integration. In our case, GIS is used as a broad concept, representing digital spatio-temporal databases. The integration between image analysis and GIS can be threefold. Firstly, GIS can be used to provide image analysis algorithms with a priori information, which is used, e.g. to restrict the search space or impose constraints. Secondly, image analysis and processing methods can be used within a GIS for data analysis, especially for raster data (e.g. buffering of a corridor by using mathematical morphology), visualisation, content-based image retrieval etc. The third case, is when image analysis and GIS fully interact, e.g. GIS information is used to guide image analysis, which extracts more complete and accurate information, which is in turn used to update the GIS database. Clearly, the last case is the most challenging and interesting one. Both authors are involved as co-chairs in the ISPRS InterCommission Working Group IV/ffl.2 "Integration of Image Analysis and GIS" that was first established in 1996, fact which shows the increasing importance of this topic within our scientific communities. The Terms of Reference (ToR) of this WG are: 1. Use of GIS data and models to support image analysis; 2. Matching of image features and GIS objects for change detection and database revision; 3. Reconciliation of object modelling used in image analysis and GIS;

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