In recent years two polarimetric decompositions methods, the A/E method (Cloude and Pottier, 1997) and the three-component decomposition method (Freeman and Durden, 1998) have become the main methods of the polarimetric synthetic aperture radar data classification in radar imagery applications. In both methods the structural composition of the land cover is modelled using the main backscatter responses, as a function of the polarised channels, the level of energy received and the associated backscatter angles. To enhance the coherence in the polarimetric decompositions, data are generally multi-looked and de-speckled. The authors have applied and compared the `Freeman' three-component decomposition followed by the Wishart complex classifier with the A/E decomposition algorithms to the de-speckled and edge-enhanced Glen Affric, fully polarimetric L-band radar data. The Glen Affric radar site was chosen for its extensive and diverse semi-natural woodlands of native Scots Pine, and its varied topography. The application results demonstrate the response of the radar classification from a semi-natural land cover on a rapid topography.

In this paper we summarise the recent results from the Glen Affric radar project, a multi-disciplinary program addressing the potential of polarimetric radar interferometry to provide vegetation structural information of importance in forest mapping and ecology studies. We present a comparison of results from L-band repeat pass SAR imagery with detailed in-situ measurements of forest height and topography.

We describe the Glen Affric radar project, a multi-disciplinary program addressing the ability of polarimetric radar interferometry to provide quantitative vegetation structural information of importance in forest mapping and ecology studies

In this paper we propose a new method for speckle filtering and derivation of an optimum interferogram from fully Polarimetric Interferometric SAR data. This method is based on the phase maps derived from the high resolution ESPRIT algorithm. The cross composite covariance matrix elements are filtered using a supervised edge aligned Lee filter. In this way the cross polarisation interference is minimised, preserving the information held in the Polarimetric phase elements. The results of the application of this method to the Glenaffric radar data are presented and show an improved phase response.