Land Cover Classification Based on Double Scatterer Model and Neural Networks

Abstract

In this paper, a supervised land cover classification is presented based on the extracted information from polarimetric synthetic aperture radar (PolSAR) images. The analysis of the polarimetric scattering matrix is accomplished according to the Double Scatterer Model which interprets each PolSAR cell by a pair of elementary scattering mechanisms. Subsequently, by utilizing the contribution rate of the two fundamental scatterers, a novel data representation is accomplished, providing great informational content. The main component of the research is to highlight the robust new feature-tool and afterwards to present a classification scheme exploiting a fully connected artificial neural network (ANN). The PolSAR images used to verify the proposed method were acquired by RADARSAT-2 and the experimental results confirm the effectiveness of the presented methodology with an overall classification accuracy of 93%, which is considered satisfactory since only four feature-vectors are used.

Keywords

• land cover classification
• polarimetric SAR
• target decomposition
• Double Scatterer Model
• neural networks
• Cameron decomposition