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Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
Dietmar Kunz
Institute of Photogrammetry and Remote Sensing, University of Karlsruhe, Englerstr. 7, D-76128 Karlsruhe, Germany
KEYWORDS: Image Analysis, ATKIS-DLM 200, Symbolic Scene Description, Disjoint Topographic Objects, Semantic
Classification, Semantic Network
The aim of this approach is to obtain an improved and automated satellite image analysis with regard to the determination of landuse
classes by means of specific knowledge, which is represented in digital topographic databases. Because of the relatively poor
geometric resolution of common satellite sensors (e.g. Landsat TM with 30 m) this project concentrates on main area-based landuse
classes like ’settlement’, forest’, ’water’ or ’agriculture’. By using integrated knowledge processing (data fusion) in feature extraction,
building of disjoint geometric objects and a classification process, the resulting semantic scene description is more accurate than with
classical methods of satellite image analysis. The pixel-oriented satellite image and the vector-oriented topographic database are two
different sources of knowledge. Both present a description of the same scene, but from different perspectives. For the comparison of
the two different knowledge bases, it is necessary to introduce methods on higher symbolic level. The results of feature extraction
supply this symbolic description of the satellite image, while the topographic database is already on this higher level. By means of an
intersection of both geometric scene descriptions from segmentation and topographic database, we obtain a new unambiguous scene
description with disjoint objects. The classification process is performed in a semantic network, which is able to process general and
specific knowledge about the topographic and disjoint objects. The use of a semantic network for satellite image analysis is new.
After the classification, disjoint objects with the same semantic meaning and a common border are merged. The result is a complete
semantic description of the scene. Experiences with the represented knowledge based analysis process confirm the efficiency of this
concept by a better separability of object classes. The determination of a good valuation function of spectral as well as non-spectral
features in the decision process that is performed in the semantic network has however proved to be a very complex and time
consuming task.
The main emphasis of this paper is placed on the
investigation and use of synergy effects between satellite
image analysis and digital topographic database. Since
several years many countries all over the world have been
building-up digital topographic databases for planning,
modelling and visualization purposes. In Germany, the
surveying administration has designed and created the
topographic database ATKIS (Authoritative Topographic
Cartographic Information System) (AdV, 1989). ATKIS is
divided into three different landscape models DLM25,
DLM200 and DLM1000, which contain mainly the objects
of the official topographical maps at scales 1:25,000,
1:200,000 and 1:1,000,000 respectively. For updating such a
given topographic database, the detail of image information
should be higher. Because of common satellite sensors with
resolution of approximately 10m to 30m, this project
concentrates on the DLM200 map information. The use of
such topographic databases can support and improve the
satellite image analysis, not only for a knowledge based
feature extraction, but also in the following process of
semantic modelling of the topographic objects contained in
the scene.
Fig. 1. Flowchart of the analysis process.
real world
digital database satellite image