Talukdar, S., Singha, P., Mahato, S., Shahfahad Pal, S., Liou, Y-A. and Rahman, A. (2020) Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12, Article No. 1135.
https://doi.org/10.3390/rs12071135

has been cited by the following article:

• TITLE: Simulated Annealing for Land Cover Classification in PolSAR Images

  AUTHORS: Georgia Koukiou

  KEYWORDS: Land Cover Classification, Simulated Annealing, Fully Polarimetric SAR, Co-herent Decomposition, Elemental Scatterers

  JOURNAL NAME: Advances in Remote Sensing, Vol.11 No.2, June 14, 2022

  ABSTRACT: Simulated Annealing (SA) is used in this work as a global optimization technique applied in discrete search spaces in order to change the characterization of pixels in a Polarimetric Synthetic Aperture Radar (PolSAR) image which have been classified with different label than the surrounding land cover type. Accordingly, Land Cover type classification is achieved with high reliability. For this purpose, an energy function is employed which is minimized by means of SA when the false classified pixels are correctly labeled. All PolSAR pixels are initially classified using 9 specifically selected types of land cover by means of Google Earth maps. Each Land Cover Type is represented by a histogram of the 8 Cameron’s elemental scatterers by means of coherent target decomposition (CTD). Each PolSAR pixel is categorized according to the local histogram of the elemental scatterers. SA is applied in the discreet space of nine land cover types. Classification results prove that the Simulated Annealing approach used is very successful for correctly separating regions with different Land Cover Types.