OBJEKTNO USMERJENA ANALIZA PODATKOV DALJINSKEGA ZAZNAVANJA

Tatjana Veljanovski, Urša Kanjir, Krištof Oštir

DOI: 10.15292/geodetski-vestnik.2011.04.641-664

 

OBJECT-BASED IMAGE ANALYSIS OF REMOTE SENSING DATA

Tatjana Veljanovski, Urša Kanjir, Krištof Oštir

DOI: 10.15292/geodetski-vestnik.2011.04.665-688

 

Izvleček:

Na področju daljinskega zaznavanja se razvijajo različne metode in tehnologije za brezkontaktno in stroškovno učinkovito izdelavo kart pokrovnosti/rabe tal na velikih območjih ter drugih tematskih kart. Osrednjega pomena za zadostno razpoložljivost in zanesljivost takšnih kart za raziskave zemeljskega površja je razvoj učinkovitih postopkov analize in klasifikacije posnetkov. Za klasifikacijo satelitskih posnetkov nizke in srednje ločljivosti (njihova prostorska ločljivost je kvečjemu primerljiva z velikostjo geografskih objektov) zadostuje uporaba pikselsko usmerjene klasifikacije, pri kateri posamični piksel razvrstimo v najprimernejši razred na podlagi njegovih spektralnih lastnosti.
Ko povečujemo prostorsko ločljivost posnetkov, pikselska klasifikacija ni več učinkovita. Bistveno se namreč spremeni razmerje med velikostjo piksla na eni ter razsežnostjo in detajlom opazovanih elementov (objektov) geografske stvarnosti na drugi strani. V zadnjem desetletju se zato vse bolj uveljavlja objektno usmerjen pristop obdelave podob. Ta združuje segmentacijo, ki je temeljna faza za razmejevanje geografskih elementov, in klasifikacijo, ki je semantično (kontekstualno) podprta. Segmentacija razdeli podobo na homogene skupine pikslov (segmente), semantična klasifikacija pa jih nato razvršča v razrede na podlagi njihovih spektralnih, geometričnih, teksturnih in drugih lastnosti. Namen prispevka je predstaviti teoretično utemeljitev in metodologijo objektno usmerjene obdelave v daljinskem zaznavanju, podati pregled stanja na področju ter opozoriti na nekatere omejitve tehničnih rešitev.

Ključne besede: daljinsko zaznavanje, objektno usmerjena analiza podob, segmentacija, objektna klasifikacija, semantična klasifikacija

 

Abstract:

Remote sensing has developed various methods and technologies for contactless and cost-effective mapping of large area land cover/land use maps and other thematic maps. The key factor for the availability and reliability of these maps for use in Earth sciences is the development of effective procedures for satellite data analysis and classification. The most appropriate approach for classifying low and medium resolution satellite images (pixel size is coarser than, or at best similar to, the size of geographical objects) is pixel-based classification in which an individual pixel is classified into the closest class based on its spectral similarity.
With increasing spatial resolution, pixel-based classification methods became less effective, since the relationship between the pixel size and the dimension of the observed objects on the Earth's surface has changed significantly. Therefore object-oriented classification has become increasingly popular over the past decade. This combines segmentation (which is a fundamental phase of the approach) and contextual classification. Segmentation divides the image into homogeneous pixel groups (segments), which are – during the semantic classification process - arranged into classes based on their spectral, geometric, textural and other features during. The intent of this paper is to present the theoretical argumentation and methodology of object-based image analysis of remote sensing data, provide an overview of the field and point out certain restrictions as regards the current operational solutions.

Keywords: remote sensing, object-based image analysis, segmentation, object-based classification, semantic classification

 

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