Suitability of Markov Random Field-Based Method for Super-Resolution Land Cover Mapping

Suitability of Markov Random Field-Based Method for Super-Resolution Land Cover Mapping

Super-resolution mapping (SRM) works by dividing the coarse pixel into sub-pixels and assign the class proportion estimated by subpixel classification to each corresponding sub-pixels then the class labelling is optimized based on the principle of spatial dependency. Among the existing SRM techniques Markov random field (MRF)-based SRM is one of the most recently introduced technique. This study attempts to assess the suitability of the technique for superresolution land cover mapping.