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

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
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 
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. 
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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