Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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, re 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) shod 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.
['Huiran Jin', 'Peijun Li']
I am writing a research paper on a idea Integration of region growing and morphological analysis with super-resolution land cover mapping
[{"from": "human", "value": "I am writing a research paper on a idea Integration of region growing and morphological analysis with super-resolution land cover mapping"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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, re 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) shod 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."}]