An approach to use polarimetric signature for land cover classification

The aim of this paper is to explore the information obtain from the fully polarimetric SAR data. Fully polarimetric SAR data contain both magnitude and phase information therefore it contains great potential about target classification. By using both parameter amplitude and phase we can distinguishes different types of scattering mechanism. For fully utilization of polarimetric SAR data, polarization signatures are used which utilizes the different orientation angle. Polarization signature is a 3-D plot of the received backscattered intensity as a function of ellipticity and orientation angle of antenna. Polarization signatures of urban area are equivalent to dihedral corner reflector which shows the double bounce, polarization signature of water is equivalent to trihedral which shows single bounce and short vegetation shows the polarizations signatures equivalent to dipole at different orientation angle. In this paper, by utilizing the fully polarimetric ALOS-PALSAR data, polarization signatures are extracted at different angles and their capability to classify different land cover classes like; urban, water, short vegetation, tall vegetation and bare soil are explored. The scattering mechanism of generated elliptical and linear polarized images for above mentioned land cover classes is also analyzed and on the basis of their scattering mechanism, decision tree classification (DTC) algorithm has been proposed and performance of the algorithm is also compared with other supervised and unsupervised classification techniques.