Semisupervised Classification of Polarimetric SAR Image.
Multi-look polarimetric SAR (synthetic aperture radar) data can be represented either in Mueller matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution.The authors use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration.Fuzzy Superpixels for Polarimetric SAR Images Classification. Abstract: Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application.
An entropy based classification scheme for land applications of polarimetric SAR.As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks.
SAR image classification with high frequency component derived from wavelet Multi-Resolution Analysis: MRA is proposed. Although it is well known that polarization signature derived from fully polarized SAR data is useful for SAR image.
