Information content of polarimetric synthetic aperture radar data

Research into the analysis of polarimetric synthetic aperture radar (SAR) data continues to reveal new applications and data extraction techniques. The objective of this thesis is to examine the information content of a quad-polarization SAR, and determine which polarimetric variables are most useful for classification purposes. The four complex polarimetric radar channels (HH, HV, VH, and VV) are expressed as nine scattering matrix cross-product "features" (with the loss of only absolute phase), and the relative utility of each for terrain classification is examined. Feature utility is examined in two ways — by measuring how each feature separates classes of terrain in an image, and by measuring how well a classifier performs with and without each feature. The features are then ranked in order of utility to the classifier, or in order of information content. A sharp distinction is found between those features that provide information useful to the classifier, and those that do not. It is found that those features that are defined as the product of a co-polarized and a cross-polarized term can be relatively safely ignored, with little loss of classification accuracy. This would be useful for reducing data transmission, storage, and processing requirements, and for designing future simplified radar systems. There is qualitative evidence that classification performance can actually be improved when these features are ignored. Of three simplified radar systems considered, the co-polarized design (returning only the complex HH and VV channels) in general produced classifications closest to that of a fully polarimetric SAR.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat