Sub-pixel Land Cover Mapping Based on Markov Random Field Models

Teerasit Kasetkasem, Manoj K. Arora, Pramod K. Varshney

Research output: Contribution to conferencePaper

3 Scopus citations

Abstract

Occurrence of mixed pixels in remote sensing images is a common phenomenon particularly in coarse spatial resolution images. In these cases, sub-pixel or soft classification may be preferred over conventional hard classification. However, sub-pixel classification fails to account for the spatial distribution of class proportions within the pixel. A better approach may be to generate a land cover map at a finer resolution from the coarse resolution images based on image models that accurately characterize the spatial distribution of the classes. The resulting fine resolution map may be called a sub-pixel or super resolution map. In this paper, an approach based on Markov random fields is introduced to generate sub-pixel land cover maps from remote sensing images dominated by mixed pixels.

Original languageEnglish (US)
Pages3456-3458
Number of pages3
StatePublished - Nov 24 2003
Event2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France
Duration: Jul 21 2003Jul 25 2003

Other

Other2003 IGARSS: Learning From Earth's Shapes and Colours
CountryFrance
CityToulouse
Period7/21/037/25/03

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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    Kasetkasem, T., Arora, M. K., & Varshney, P. K. (2003). Sub-pixel Land Cover Mapping Based on Markov Random Field Models. 3456-3458. Paper presented at 2003 IGARSS: Learning From Earth's Shapes and Colours, Toulouse, France.