Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 11-16, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-11-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 11-16, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-11-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

AUTOMATIC CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGERY – A CASE STUDY FOR URBAN AREAS IN THE KINGDOM OF SAUDI ARABIA

A. Maas1, M. Alrajhi2, A. Alobeid2, and C. Heipke1 A. Maas et al.
  • 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany
  • 2Dept. of Surveying and Mapping, Ministry of Municipal and Rural Affairs, Riyadh Olaya KSA

Abstract. 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.