Polarimetric detection and estimation of ship wakes

Classification of Earth natural components within a full polarimetric SAR image is one of the most important applications of radar polarimetry in remote sensing. An unsupervised classification procedure, based on neural networks with competitive architectures, is applied to the full polarimetric SAR images of sea surface and ship wakes for segmentation and clustering of different ocean components. The linear feature vector used during the classification procedure is defined from a new scheme for parameterising polarimetric scattering problems, which has application in the quantitative analysis of polarimetric SAR data. The method relies on an eigenvalue analysis of the coherency matrix and employs a 3-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data