Automatic classification of high resolution satellite imagery - A case study for urban areas in the Kingdom of Saudi Arabia

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Maas, A.; Alrajhi, M.; Alobeid, A.; Heipke, C.: Automatic classification of high resolution satellite imagery - A case study for urban areas in the Kingdom of Saudi Arabia. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (2017), Nr. 1W1, S. 11-16. DOI:

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Sum total of downloads: 240

Updating topographic geospatial databases is often performed based on current remotely sensed images. To automatically extract the object information (labels) from the images, supervised classifiers are being employed. Decisions to be taken in this process concern the definition of the classes which should be recognised, the features to describe each class and the training data necessary in the learning part of classification. With a view to large scale topographic databases for fast developing urban areas in the Kingdom of Saudi Arabia we conducted a case study, which investigated the following two questions: (a) which set of features is best suitable for the classification?; (b) what is the added value of height information, e.g. derived from stereo imagery? Using stereoscopic GeoEye and Ikonos satellite data we investigate these two questions based on our research on label tolerant classification using logistic regression and partly incorrect training data. We show that in between five and ten features can be recommended to obtain a stable solution, that height information consistently yields an improved overall classification accuracy of about 5%, and that label noise can be successfully modelled and thus only marginally influences the classification results.
License of this version: CC BY 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2017
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 173 72.08%
2 image of flag of United States United States 21 8.75%
3 image of flag of Saudi Arabia Saudi Arabia 7 2.92%
4 image of flag of China China 5 2.08%
5 image of flag of No geo information available No geo information available 4 1.67%
6 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 3 1.25%
7 image of flag of Canada Canada 3 1.25%
8 image of flag of Slovakia Slovakia 2 0.83%
9 image of flag of Nepal Nepal 2 0.83%
10 image of flag of Austria Austria 1 0.42%
    other countries 19 7.92%

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