Cameron Decomposition Applied to Polarimetric Synthetic Aperture Radar Signature Detection

Cameron et al, have developed a method of decomposing scatterer scattering matrices based on Huynen’s scattering matrix decomposition parameters. Huynen’s decomposition parameters are not unique, in that certain transformation properties, such as the angle of symmetry itself, may misidentify a scattering matriix and therefore misidentify a particular geometric shape. Cameron decomposition derives these parameters by direct calculations from the scatterer itself so that they may not be inferred or interpreted. A detailed explanation of the foundations and method of Cameron decomposition is presented here. A description of how Cameron decomposition is implemented in Polarimetric Synthetic Aperture Radar (PolSAR) signature detection is also presented

Phase History Decomposition for Efficient Scatterer Classification in SAR Imagery

A new theory and algorithm for scatterer classification in SAR imagery is presented. The automated classification process is operationally efficient compared to existing image segmentation methods requiring human supervision. The algorithm reconstructs coarse resolution subimages from subdomains of the SAR phase history. It analyzes local peaks in the subimages to determine locations and geometric shapes of scatterers in the scene. Scatterer locations are indicated by the presence of a stable peak in all subimages for a given subaperture, while scatterer shapes are indicated by changes in pixel intensity. A new multi-peak model is developed from physical models of electromagnetic scattering to predict how pixel intensities behave for different scatterer shapes. The algorithm uses a least squares classifier to match observed pixel behavior to the model. Classification accuracy improves with increasing fractional bandwidth and is subject to the high-frequency and wide-aperture approximations of the multi-peak model. For superior computational efficiency, an integrated fast SAR imaging technique is developed to combine the coarse resolution subimages into a final SAR image having fine resolution. Finally, classification results are overlaid on the SAR image so that analysts can deduce the significance of the scatterer shape information within the image context