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

Huiran Jin, Peijun Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5099-5102
Number of pages4
ISBN (Electronic)9781509033324
DOIs
StatePublished - Nov 1 2016
Externally publishedYes
Event36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
Duration: Jul 10 2016Jul 15 2016

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2016-November

Other

Other36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
CountryChina
CityBeijing
Period7/10/167/15/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Keywords

  • Morphological analysis
  • Probability maps
  • Region Growing
  • Super-resolution mapping (SRM)

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