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.
Highlights
Land cover classification is a very interesting field of research with numerous applications, among them land use and land cover changes [1] [2]
Land cover classification was carried out in this work by mean of two powerful tools, namely: Cameron coherent target decomposition to represent each pixel of the synthetic aperture radar (SAR) image with an elementary scattering mechanism and Markov chain models to record the alternation from scattering mechanism to scattering mechanism along the image
The transition matrices corresponding to the Markov models constitute the feature classification tool
Summary
Land cover classification is a very interesting field of research with numerous applications, among them land use and land cover changes [1] [2]. Automatic classification of the dominant scattering mechanisms associated with the pixels of polarimetric SAR images is carried out in [15]. The work in [25] showed that studying the alternation of the elementary scatterers ships can be and with high reliability distinguished from the sea background This approach was based on an innovative CFAR detection scheme. Each separate pixel of the fully polarimetric SAR image is represented by one of the elementary scatterers, which arise from Cameron CTD. 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 but the way these types of scatterers alternate from pixel to pixel on the SAR image.
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