Integration of region growing and morphological analysis with super-resolution land cover mapping

Converting probability maps derived from indicator cokriging (ICK) to a specific land cover classification map is the second step of super-resolution mapping (SRM) under the geostatistical framework. In this study, two image segmentation strategies, namely mathematical morphology and region growing, were applied on the ICK-derived probability maps in order to take into account spatial characteristics such as shape and connectivity. A case study in South Carolina (USA) showed that the thematic map created by the proposed method had an overall accuracy improved by 2% and Kappa improved by 6% compared to the map derived from the existing sequential generation process. This indicates our methodology as a promising alternative that can be embedded into SRM tasks.