An MRF based approach for simultanous land cover mapping and cast shadow removal

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

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

Occurrence of shadowy pixels in remote sensing images is a common phenomenon particularly with passive sensors. In these cases, analysts may treat these pixels as a separate land cover class. This may result in the loss of information present in the shadowy pixels A better approach may be to correct light intensity values in shadowy pixels and use the light-corrected image to produce a land cover map. Most light intensity correction algorithms are not designed to optimize the classification performance. Consequently, the accuracy of a resulting land cover map may be degraded. As a result, this paper proposes a new approach to simultaneously determine the land cover map and determine the light intensity value of shadowy pixels based on a Markov random field model. With this approach, the light intensity correction is performed such that the classification accuracy is maximized. The outputs of the proposed algorithm are a land cover map and shadow-free remote sensing image.

Original languageEnglish (US)
Title of host publication2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Pages2133-2136
Number of pages4
DOIs
StatePublished - 2006
Event2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS - Denver, CO, United States
Duration: Jul 31 2006Aug 4 2006

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Country/TerritoryUnited States
CityDenver, CO
Period7/31/068/4/06

Keywords

  • Land cover mapping
  • Markov random field
  • Remote sensing
  • Shadow-removal
  • Simulated annealing

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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