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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B1, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
2. THE WiDOP PLATFORM
Integrating knowledge into a processing strategy provides much
needed flexibility. However, it is clear that knowledge varies at
different stages. This variation depends mainly on type,
amount, and the quality of knowledge available during
processing, as well as on the ability to connect different sources
and domains of knowledge (related to objects, algorithms,
scenes, data and so on). Additionally, knowledge should
increase step by step based on the quality of results collected
from concrete applications. Success will also clearly increase
with increasing amounts of available knowledge. We therefore
distinguish different scenarios.
2.1. The Scenarios
2.1.1. Known objects, known positions
Detailed knowledge (exact positions and characteristics) of
objects already exists in such cases. The knowledge base
(“KB”) supports the processing for verification.
2.1.2. Known objects, unknown positions
This case reflects a typical situation, in which knowledge about
scene objects exists but not their location in the data. The KB
that provides the scene knowledge interacts with the processing
knowledge to determine the probable sequences that detect the
objects and derive their location.
2.1.3. Unknown objects, unknown positions
This is the most complex case, in which only generic
knowledge about the scene exists. In such cases, the KB
recommends the detected geometries to their object types
through examining the semantics defined against them.
2.2. The Iterative Approach of Classification
This approach is used to derive concrete detection from a
generic base. We call it the “Iterative Semantic Classification
Method” or ISCM. Semantic Figure 1 illustrates the iteration
method. Details on it will be presented in further sections.
Knowledge Knowledge
Processing
hi
Detected ijscis
Figure . ISCM (a) Basic knowledge framework (b) Knowledge
population
The initial knowledge is mainly a schema that represents the
scene and the processing knowledge. It is hence not a concrete
knowledge source (fig.la). It has to be enriched with real
objects in the course of the iterative process. The knowledge is
refined after every step of processing, through the population of
the results into the knowledge schema. It thus transforms the
knowledge schema into a concrete and comprehensive
knowledge base (fig. 1b).
91
2.2.1. Knowledge Domains
Building on the works of Pu (2010) and Maillot & Thonnat
(2008), knowledge of algorithmic processing is related to that
of objects in the scene in order to support their detection. In this
manner, the mapping of algorithmic knowledge to the scene and
objects can infer processing, and determines which algorithms
are best suited for any particular characteristic of the objects.
This process makes the methodology scene independent.
The knowledge domains of Algorithms and Scene are mapped
through rules, which are related to geometry, topology etc.
These mappings infer best suitable algorithms or algorithmic
sequence for detecting geometries. Once detected, they are
related to their corresponding objects inside the KB. The
preexisting scene knowledge is then used for verification.
Beside these two, other supporting knowledge domains provide
significant supports. They are seamlessly integrated within the
knowledge schema through their semantic interpretation and
relationship to the main knowledge domains.
The solution is based on knowledge technologies of the
Semantic Web (Berners-Lee, 1998) framework. The WiDOP
platform uses knowledge technologies like Web Ontology
Language (Bechhofer, et al., 2004), (Patel-Schneider, Hayes, &
Horrocks, 2004) or the Semantic Web Rule Language (SWRL)
(Horrocks, et al., 2004). The knowledge equations used here are
based on Description Logics (DL) which is core in the rapid
development of the knowledge technologies. The next section
discusses the ontology schema of the WiDOP platform
(expressed in OWL) to demonstrate its robustness to adapt into
any structural domain.
2.2.2. Ontology Schema
The top level knowledge is illustrated in figure 2. The top level
classes of algorithmic and scene knowledge are represented
through the top level classes Algorithms and DomainConcept
respectively. The class Algorithms constitutes the algorithmic
knowledge through a taxonomical hierarchy, and semantic rules
through restrictions. Similarly, the class DomainConcept
presents the scene knowledge through a hierarchical structure
reflecting the objects in the scene and semantic rules.
The basic ontology schema provides an overview of the scene
and processing knowledge, defining what knowledge exists in
different domains and how they are interrelated. They are
defined by rules which facilitate selecting the algorithms and
define the strategy to detect the objects in the scene.
- TEENS Re,
AZ
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( Algorithms ) LEN € =.
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N
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N
poe —
Figure . General overview of ontology schema
