Introduction
UML Class Diagram
Satellite Image Modelling
Satellite Image Classification Modelling
Model Transformation Process
OWL/XML Ontology
Introduction
SOLERES-KRS is the part of the SOLERES project that models
environmental information. Modelled environmental information is linked
to satellite image classification. The aim of satellite image
classification is to divide image pixels into discrete classes
(spectral classes). The resulting classified image is a thematic map of
the original image essentially. Figure shows the classification process
.
Figure 1. Classification process.
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UML Class Diagram
Figure shows the UML class diagram of a satellite image
classification modelled partially. The concepts are basically grouped
into two parts: (a) the concepts that identify the satellite image
information (Satellite_image class) and; (b) the concepts that
identify the process of an image classification (Image_classification
class).
Figure 2. UML Class Diagram. Click to enlarge
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Satellite Image Modelling
A satellite image is obtained by means of a satellite (remote
sensing). This information is modelled in the diagram by the Satellite
class, and it includes properties like its name and the agency’s name,
among others. In addition, a satellite uses a set of instruments or
sensors (Instrument class) to obtain the satellite image in a
certain resolution (Resolution class) and by using a set of
bands (Band class). The resolution describes the spectral,
radiometric, temporary and spatial information of an image. The same
band of a satellite image can be got by different instruments. Finally,
every satellite image can be physically stored as an external link in a
set of files, identified in the Resource class.
Figure 3. Satellite Image Modelling. Click to enlarge
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Satellite Image Classification
Modelling
We represent the concepts that are associated with the information
of an image classification, grouped into the Image_classification
class. A classification is made by some technicians (Technician
class) in some specific time (Time class) and it uses a
training set (Training_set class) and a performance indicator
evaluation method (Performance_indicator class) to obtain
classes (Classes class) that generate the classified image (Classified_image
class). Both of them, training set and classified image, are stored in
files, too.
There are other evaluation methods for the image classification,
such as Ellipses, Histogram, Transformed divergence, Jeffries-Martusa
distance, Statistical divergence. However, the evaluation method used
in the SOLERES project is performance indicator. Common classification
procedures can be split up into two divisions based on the method used:
supervised and unsupervised classification.
There are several types of statistics-based supervised
classification algorithms. Some of the most popular ones are parallelepiped,
minimum distance, maximum likelihood, fuzzy
supervised, neural model and Mahalanobis distance,
among
others.
In
an unsupervised classification, the analyst only
specifies the number of classes, and the algorithm groups the classes,
only based on the numerical information in the data. In these
algorithms, the analyst does not have to know the zone to study. There
are many unsupervised classification algorithms, such as Isodata,
k-means, Leader, neural model unsupervised
or MaxiMin. This information is identified in the UML class
diagram with the Classificator_type and Training_type
enumeration classes.
Figure 4. Satellite Image
Classification
Modelling. Click to enlarge
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Model Transformation Process
The ontology has been created in OWL/XML from the UML class diagram
automatically. Figure shows the UML to OWL/XML transformation schema.
We used the Model-Driven Engineering (MDE) perspective based on the OMG
classical Model-driven Architecture (MDA). To achieve the process we
used three models: the UML model, the OWL model and the OWL/XML model.
The first one is used to represent the environmental image
classification class diagram. The second one is generated temporarily
to be able to carry out the transformation. The last one represents the
OWL ontology obtained in XML format.
The model-transformation is carried out automatically in two steps:
(a) in the first step, we used the UML2OWL ATL transformation to obtain
an OWL model for the ontology by using the model of the UML class
diagram, and; (b) in the second step, we got the XML final
representation of our OWL ontology through the model obtained and the
OWL2XML ATL transformation. Figure shows a piece of the OWL/XML model
obtained as a result of the mapping process.
Figure 5. UML to OWL/XML
transformation
schema.
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OWL/XML Ontology
This example shows the definition of the Image_classification
class. It has two parts: a reference generated automatically by the
transformation process and a list of restrictions. The reference is
made up of the ID element. The list of restrictions includes
classification characteristics, such as id (Image_classification.id)
or
name
(Image_classifica-tion.name) properties, and relations
with instances of other classes, such as Technician (Image_classification.ima-ge_classif_tech)
or
Performance_indicator (Image_classification.perform_indicator).
Moreover,
the
cardinality restriction asserts that every
classification has exactly one id, one name, etc.
Finally, the label element provides a readable name for the
class. The definition of the other classes would be similar to this one.
Figure 6. UML Class Diagram and a
piece of
the OWL/XML Ontology. Click
to
enlarge
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