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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
layer. Topological relations provide information about the kind
and the properties of neighboured objects. Therefore, the class of
attributed relations (attr-rel) is introduced. In contrast to other
relations, this one has attributes which can be used to constrain
the properties of the connected nodes. For example, a topological
relation close-to can be generated which restricts the position of
an object to its immediate neighbourhood. The initial concepts
which can be extracted directly from the data are connected via
the data-of link to the primitives segmented by image processing
For the efficient representation of multiple relations, the
minimum and maximum number of edges can be defined in the
knowledge base. The minimum quantity describes the number of
obligatory relations and the difference to the maximum quantity
represents the number of optional relations between objects. In
this way, it can be easily modelled that for example a crossroad
consists of three up to five intersecting roads. Additionally, for
each edge a priority can be defined in order to realize an ordered
evaluation of the relations. Edges with high priority are
instantiated first. For the application of landscape analysis for
example, it can be guaranteed that the streets are extracted prior
to the rivers.
Some relations appear exclusively in certain domains. For
example roads have always a lane but they have pavements in
urban areas only. This fact is taken into consideration by a
domain dependent relation in the generic model. Fig. 1 shows a
simple semantic net for a generic model of a Road Net which is
defined as a composition of at least one Road, illustrated by the
set [1,«]. A Road consists of one or two lanes. Its specialization
Major Road inherits the properties of Road and possesses an
additional Crash Barrier. For the part-of relation between
pavement and road the domain Urban Scene is defined. Only in
urban scenes this relation is valid and the system searches for
pavements. All the initial objects Crash Barrier, Lane, and
Pavement are represented by a Stripe-Form in the image.
Figure 1. Example for a semantic net: The scene contains at least one
Road. The Pavement is defined for the domain Urban
Scene. The more special concept Major Road inherits the
properties of Road. All objects are represented by a
Stripe-Form in the image.
2.2. Control of the Scene Analysis
To make use of the knowledge represented in the semantic net
control knowledge is required that states how and in which order
scene analysis has to proceed. The control knowledge is
represented explicitly by a set of rules. The rule for instantiation
for example changes the state of an instance from hypothesis to
complete instance, if all subnodes, which are defined as
obligatory in the concept net, have been instantiated completely.
If an obligatory subnode could not be detected, the parent node
becomes a missing instance.
An inference engine determines the sequence of rule execution
according to a given strategy. A strategy contains a set of rules out
of the rule base. For each valid rule, a priority is defined to
determine in which order the rules are tested. The first matching
rule is fired. The user can modify the interpretation strategy by
changing the priorities and by removing or inserting rules to the
current strategy. The default strategy prefers a model-driven
interpretation with a data-driven verification of hypotheses.
Topological relations are instantiated as soon as possible to
realize a spatial reasoning.
Whenever ambiguous interpretations occur, for example if more
than one suitable image primitive is found for a hypothesis, they
are treated as competing alternatives and stored in the leaf nodes
of a search tree. Each alternative is judged by comparing the
measured object properties with the expected ones. The
judgement calculus models imprecision by fuzzy sets and
considers uncertainties by distinguishing the degrees of
necessity and possibility (Dubois, 1988; Tonjes, 1999). The
judgements of attributes and nodes are fused to a judgement of
the whole interpretation. The best judged alternative is selected
for further investigation.
Starting at the root node of the concept net, the system generates
model-driven hypotheses for scene objects and verifies them
consecutively in the data. Expectations about scene objects are
translated into expected properties of the corresponding image
primitives to be extracted from the sensor data. Suitable image
processing algorithms are activated and the semantic net assigns
a semantic meaning to the returned primitives in a data-driven
way. Interpretation stops, if a given goal concept is instantiated
completely or no further rule of the current strategy can be fired.
For object extraction, only those features are relevant that can be
observed by the sensor and that give a hint for the presence of the
object to be extracted. Hence, the knowledge base contains only
the necessary and visible object classes and properties. The
network language described in chapter 2.1. is used to represent
the prior knowledge by a semantic net. In Figure 2 a generic
model for the interpretation of remote sensing images is shown. It
is divided into the 3D scene domain and the 2D image domain.
The 3D scene domain splits into the semantic layer and the
physical layer. If a geoinformation system (GIS) is available and
applicable, an additional GIS layer can be defined representing