Classification of pansharpened urban satellite images

Frosti Pálsson, Johannes R. Sveinsson*, Jon Atli Benediktsson, Henrik Aanæs

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)


The classification of high resolution urban remote sensing imagery is addressed with the focus on classification of imagery that has been pansharpened by a number of different pansharpening methods. The pansharpening process introduces some spectral and spatial distortions in the resulting fused multispectral image, the amount of which highly varies depending on which pansharpening technique is used. In the majority of the pansharpening techniques that have been proposed, there is a compromise between the spatial enhancement and the spectral consistency. Here we study the effects of the spectral and spatial distortions on the accuracy in classification of pansharpened imagery. We also study the performance in terms of accuracy of the various pansharpening techniques during classification with spatial information, obtained using mathematical morphology (MM). MM is used to derive local spatial information from the panchromatic data. Random Forests (RF) and Support Vector Machines (SVM) will be used as classifiers. Experiments are done for three different datasets that have been obtained by two different imaging sensors, IKONOS and QuickBird. These sensors deliver multispectral images that have four bands, R, G, B and near infrared (NIR). To further study the contribution of the NIR band, experiments are done using both the RGB bands and all four bands, respectively.

Original languageEnglish
Article number6096424
Pages (from-to)281-297
Number of pages17
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number1
Publication statusPublished - Feb 2012

Bibliographical note

Funding Information:
Manuscript received June 30, 2011; revised September 14, 2011; accepted September 19, 2011. Date of publication December 07, 2011; date of current version February 29, 2012. This work was supported by the Research Fund of the University of Iceland. F. Pálsson, J. R. Sveinsson, and J. A. Benediktsson are with the Faculty of Electrical and Computer Engineering, University of Iceland, 107 Reykjavik, Iceland (corresponding author, e-mail: [email protected]). H. Aanæs is with the Department of Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Lyngby, Denmark. Color versions of one or more of the figures in this paper are available online at Digital Object Identifier 10.1109/JSTARS.2011.2176467

Other keywords

  • Classification
  • mathematical morphology
  • morphological profile
  • pansharpening
  • spatial consistency
  • spectral consistency


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