Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images
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Many public and private decisions rely on geospatial information stored in a GIS database. For good decisionmaking this information has to be complete, consistent, accurate and up-to-date. In this paper weintroduce a new approach for the semi-automatic verification of a specific part of the, possibly outdatedGIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR)multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classificationbased on a Markov Random Field is employed to extract image regions which contain agriculturalareas (without distinction between cropland and grassland), and these regions are intersected withboundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into differenthomogeneous regions by means of image segmentation, followed by a classification of thesesegments into either cropland or grassland using a Support Vector Machine. The classification result ofall segments belonging to one GIS object are finally merged and compared with the GIS database label.The developed approach was tested on a number of images. The evaluation shows that errors in theGIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process.
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