Land Cover Classification Using E-sar Polarimetric Data

Different decomposition approaches have been proposed in order to analyse and interpret SAR polarimetric images. These are based either on the complex voltage reflection matrix, like Pauli, or on power reflection matrix, like the covariance or coherency matrix. They produce polarimetric parameters which are appropriate to retrieve information on the scattering process of the target. If the target is distributed, polarimetric parameters are affected by speckle. The objectives of this work are to search and point out the parameters most appropriate for the interpretation of different land uses in a ESAR image; to evaluate Maximum Likelihood (ML) classification results produced by two different polarimetric input sets: the full polarimetric, and the Pauli images; to investigate the most appropriate size of the Lee filter window for polarimetric speckle reduction. Based on the full polarimetric L-band, polarization signatures were extracted and analyzed for four land cover classes: urban, forest, vegetation and smooth surfaces. The scattering mechanism of these land cover classes was also analysed based on the images generated by Pauli decomposition analysis. The Maximum Likelihood classification was performed on the “magnitude content” of the a) original polarimetric data, b) images produced by the Pauli analysis, and c) both previous cases data. The accuracy of each class confirmed the contribution of polarimetric data and Pauli parameters in the interpretation of the scattering mechanism. To reduce speckle effects and improve classification results, the Lee filter was applied on the above images several times, each time increasing the size of the moving window. The ML classification was performed on the despeckled images. Classification accuracy pointed out the most appropriate size of the filter window for speckle reduction.