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

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3^4 June, 1999
133
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Figure 2. Semantic net representing a generic model of a purification plant and its relation to the image data.
the scene specific knowledge from the GIS. The 2D image
domain contains the sensor layers adapted to the current sensors
and the data layer.
For the objects of the 2D image domain, general knowledge
about the sensors and methods for the extraction and grouping of
image primitives like lines and regions is needed. The primitives
are extracted by image processing algorithms and they are stored
in the semantic net as instances of the concepts Line Data or
Region Data respectively. Due to fragmentation, the lines and
regions have to be grouped according to perceptual criteria like
continuity, nearness, similarity etc. A continuous Stripe for
example is represented in the semantic net by a composition of
neighbouring SubStripes. The sensor layer can be adapted to the
current sensor type like SAR, IR or optical sensor. For a
multisensor analysis, the layer is duplicated for each new sensor
type to be interpreted, assuming that each object can be observed
in all the images (see Fig. 2). All information of the 2D image
domain is given related to the image coordinate system. As each
transformation between image and scene domain is determined
by the sensor type and its projection parameters, the
transformations are modelled explicitly in the semantic net by the
concept Sensor and its specializations for the different sensor
types.
The knowledge about inherent and sensor independent
properties of objects are represented in the 3D scene domain
which is subdivided into the physical, the GIS and the semantic
layer. The physical layer contains the geometric and radiometric
properties as basis for the sensor specific projection. Hence, it
forms the interface to the sensor layer(s). The semantic layer
represents the most abstract layer where the scene objects with
their symbolic meanings are stored.
The semantic net eases the formulation of hierarchical and
topological relations between objects. Thus, it is possible to
describe complex objects like a purification plant as a
composition of sedimentation tanks and buildings close to a road
and a river, where the cleaned water is drained off (see Fig. 2).
The symbolic objects are specified more concretely by their
geometry and material. In conjunction with the known sensor
type, the geometrical and radiometrical appearance of the objects
in the image can be predicted. This prediction can be improved, if
GIS data of the observation area is available. Though the GIS
may be out of date, it represents a partial interpretation of the
scene providing semantic information. Hence, the GIS objects
are connected directly with the objects of the semantic layer.
4. INTERPRETATION OF MULTISENSOR IMAGES
The automatic analysis of multisensor images requires the fusion
of sensor data. The presented concept, to separate strictly the
sensor-independent knowledge of the 3D scene domain from the
sensor-dependent knowledge in the 2D image domain, eases the