PolSAR Models with Multimodal Intensities

Polarimetric synthetic aperture radar (PolSAR) systems are an important remote sensing tool. Such can provide high spacial resolution images, but they contaminated by interference pattern called multidimensional speckle. This fact requires that PolSAR images receive specialised treatment; particularly, tailored models which close to physical formation sought. In this paper, we propose two new matrix arise from applying the stochastic summation approach PolSAR, compound truncated Poisson complex Wishart (CTPCW) and geometric (CGCW) distributions. These offer unique ability express multimodal data. Some of their mathematical properties derived discussed—characteristic function Mellin-kind log-cumulants (MLCs). Moreover, maximum likelihood (ML) estimation procedures via expectation maximisation algorithm for CTPCW CGCW parameters furnished as well MLC-based goodness-of-fit graphical tools. Monte Carlo experiment results indicate ML estimates perform at what is asymptotically expected (small bias mean square error) even small sample sizes. Finally, our proposals employed describe actual presenting evidence outperform other well-known distributions, such WmC, Gm0, Km.