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Tong, X.Y., Shen, H., Li, S., You, S., Zhang, L., et al. (2020) Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models. Remote Sensing of Environment, 237, Article ID: 111322.
https://doi.org/10.1016/j.rse.2019.111322

has been cited by the following article:

• TITLE: Fully Polarimetric Land Cover Classification Based on Markov Chains

  AUTHORS: Georgia Koukiou, Vassilis Anastassopoulos

  KEYWORDS: Fully Polarimetric SAR, Coherent Decomposition, Elementary Scatterers, Markov Chains, Land Cover Classification

  JOURNAL NAME: Advances in Remote Sensing, Vol.10 No.3, July 29, 2021

  ABSTRACT: A novel 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 land cover pixel by pixel. Cameron’s CTD is employed since it provides a complete set of elementary scattering mechanisms to describe the physical properties of the scatterer. The novelty of the proposed land classification approach lies on the fact that the features used for classification are not the types of the elementary scatterers themselves, but the way these types of scatterers alternate from pixel to pixel on the SAR image. Thus, transition matrices that represent local Markov models are used as classification features for land cover classification. The classification rule employs only the most important transitions for decision making. The Frobenius inner product is employed as similarity measure. Ten different types of land cover are used for testing the proposed method. In this aspect, the classification performance is significantly high.