Geoinformatics & Geostatistics: An OverviewISSN: 2327-4581

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Research Article, Geoinfor Geostat An Overview S Vol: 0 Issue: 1

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

Rahel Hailu Kassaye*
Ministry of Urban Development & Construction, Addis Ababa, Ethiopia
Corresponding author : Rahel Hailu Kassaye
P.O. Box 3221, Addis Ababa, Ethiopia
Tel: +251-911-809-340, Fax: +251-115-540-630;
E-mail: [email protected], [email protected]
Received: March 29, 2013 Accepted: July 12, 2013 Published: July 23, 2013
Citation: Kassaye RH (2013) Suitability of Markov Random Field-Based Method for Super-Resolution Land Cover Mapping. Geoinfor Geostat: An Overview S1. doi:10.4172/2327-4581.S1-012

Abstract

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.

Keywords: SRM; MRF; Neighbourhood; Class separability; Scale factor; Smoothing parameter

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