Conditional Random Fields for Land Use/Land Cover Classification and Complex Region Detection

Can G., Firat O., YARMAN VURAL F. T.

Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition (SSPR) / International Workshop on Statistical Techniques in Pattern Recognition (SPR), Hiroshima, Japan, 7 - 09 November 2012, vol.7626, pp.216-224 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 7626
  • City: Hiroshima
  • Country: Japan
  • Page Numbers: pp.216-224
  • Middle East Technical University Affiliated: Yes


Developing a complex region detection algorithm that is aware of its contextual relations with several classes necessitates statistical frameworks that can encode contextual relations rather than simple rule-based applications or heuristics. In this study, we present a conditional random field (CRF) model that is generated over the results of a robust local discriminative classifier in order to reveal contextual relations of complex objects and land use/land cover (LULC) classes. The proposed CRF model encodes the contextual relation between the LULC classes and complex regions (airfields) as well as updates labels of the discriminative classifier and labels the complex region in a unified framework. The significance of the developed model is that it does not need any explicit parameters and/or thresholds along with heuristics or expert rules.