Ontology based description of satellite imageries for application ...

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epresented via the most common terminologies and ideas that are acceptable among differentspecific communities. In one domain, the ontology defines a set of representational terms which arecalled concepts; it also concludes constraints and rules to preserve the consistency of domain. A wellknown definition is “an ontology is a formal explicit specification of a shared conceptualization”[Gruber 1993]WavelengthSensorhasTimeresolutionCan observeReal worldobjectsProduct ofSpatialresolutionFocus onImageriesProvide toFigure 1Conceptual frame workUserThe objective of this approach is to approximate satellite data to the user requirements (which aremapped in the application domain). An ontology is built to describe sensor properties and userrequirements in a machine readable way. A reasoner can be used in order to return the appropriateresults to user queries based on the definitions in the ontology. Ontology is built based on OntologyWeb Language (OWL) .The approach is depicted in figure 1.In this approach, sensors are described by properties, such as type (optical or microwave),wavelength (visible, near infrared etc.), temporal resolution (high-, middle- and low temporalresolution), and spatial resolution (high-, middle- and low spatial resolution). These descriptionswere collected from [Satellite Imaging Corporation]. Potential applications of satellite imageriesrelated to specific sensor properties are shown in table 1 [NASA, Navalgund 2007, Wang 2010].Each sensor type is defined as a class, in which satellite images are individuals (e.g. Landsat,Quickbird, TerraSAR-X etc.). Each image has properties related to subclasses of three attributiveclasses (WaveLength, TemporalResolution and SpatialResolution). These properties arehasWaveLength, hasTemporalResolution and hasSpatialResolution.Copyright 2011 Shaker Verlag Aachen, ISBN: 978-3-8440-0451-9
Channel/WavelengthSpatialResolution(m)TemporalResolution(day)Swath (km)ApplicationOpticalVNIR, SWIR 20 – 30 15 – 25 100 – 200 Vegetation (forestry,agriculture),coastal zone and inland waters,soil,geology, Multiple cropforecastingVNIR, SWIR 2.5 -10< 1VNIR, MIR,TIRImaging stereopairsRadarL (~23cm)C (~3 – 7 cm)90250 – 10001000 - 4000Urban applicationDaily ~1000 Ocean color, Sea surfacetemperature Regional vegetationmonitoring; atmospheric, cloude(weather), parameter retrieval2.5 – 15 ~100 Elevation mapping, 3-Dmodelling3 – 100 30 - 35 Water mask, flood, cropmonitoring, forestry, surfaceland deformationTable 1.Relationship between specific satellite imagery characteristics and applicationsFurthermore, Satellite images also have properties such as processing level, spatial coverage andacquisition date (Fig 2.). Data domain is organized by classes, subclasses and restrictions in order topreserve the consistency of domain, expressed as the follows.• Quickbird and Landsat belong to Optical class.• Optical and Microwave type must have spatial-, temporal resolution and wavelengthproperties.• Each observation only has exactly one spatial-, temporal resolution. EtcObjects that can be observed using satellite images are also defined by classes and subclasses (Figure3.) and they are described regarding to user requirements, for example forest belongs to land usegroup as part of the application domain. In this case, the domain was defined by two main classes i.e.Natural and man-made classes (Figure 3.). They have many subclasses regarding to possibleapplications and real world objects. The hierarchy of classes presents relationships between observedobject and possible applications of user interest.Copyright 2011 Shaker Verlag Aachen, ISBN: 978-3-8440-0451-9
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Page 7: These rules are easy to extend when

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