Kussul, N., Shelestov, A. and Skakun, S. (2011) Flood Monitoring from SAR Data. NATO Science for Peace and Security Series C: Environmental Security, Springer Science + Business Media B.V., Berlin.
https://doi.org/10.1007/978-90-481-9618-0_3

has been cited by the following article:

• TITLE: Fully Polarimetric Land Cover Classification Based on Hidden Markov Models Trained with Multiple Observations

  AUTHORS: Konstantinos Karachristos, Georgia Koukiou, Vassilis Anastassopoulos

  KEYWORDS: Fully Polarimetric SAR, Coherent Decomposition, Land Cover Classification, Hidden Markov Models, Remote Sensing

  JOURNAL NAME: Advances in Remote Sensing, Vol.10 No.3, September 30, 2021

  ABSTRACT: A land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize each pixel, using a set of canonical scattering mechanisms in order to describe the physical properties of the scatterer. The novelty of the proposed classification approach lies on the use of Hidden Markov Models (HMM) to uniquely characterize each type of land cover. The motivation to this approach is the investigation of the alternation between scattering mechanisms from SAR pixel to pixel. Depending on the observations-scattering mechanisms and exploiting the transitions between the scattering mechanisms we decide upon the HMM-land cover type. The classification process is based on the likelihood of observation sequences been evaluated by each model. The performance of the classification approach is assessed my means of fully polarimetric SLC SAR data from the broader area of Vancouver, Canada and was found satisfactory, reaching a success from 87% to over 99%.