Conference Agenda

The use of polarimetric Synthetic Aperture Radar (PolSAR) has the capability to improve detection and bio-physical parameters extraction in many remote sensing applications compared to the use of a single polarisation channel. The scattering matrix or the scattering vector allow to analyse the polarimetric information of targets. Unfortunately, when dealing with distributed targets, speckle introduces a statistical variation on the observed polarimetric behaviour and we need to use statistical tools. A typical solution is to extract the second order statistics building a covariance matrix [1].

The covariance matrix is formed by performing an averaged outer product of the scattering vector with itself which imposes constraints on the power distribution in the polarimetric space (i.e. the shape of the surface drawn by varying the projection vector in the matrix quadratic form). The surface is forced to be an ellipsoid with 3 main axes (in quad-pol after reciprocity and monostatic sensor assumptions).

In the past we showed how indiscriminate averaging can produce a loss of information when we are in a situation of not fully developed speckle (e.g. the distribution of the covariance matrix is not Wishart). We proposed an alternative way to decompose the partial target into a sum of low entropy components which does not require indiscriminate pre-averaging. This allows us to extract more than 3 scattering mechanisms and it does not force orthogonality among them. This is performed doing a search in the tensor field based on angular distances. We also demonstrated its usefulness by using both Monte Carlo simulations and real quad-pol ALOS-2 and RADARSAT-2 data. The simulations also show that the new methodology is able to identify the low entropy components even if the composing scattering mechanisms are not orthogonal to each other.

In this study, we introduce two main innovations. First, we extend the extraction methodology by using a modified K-means clustering algorithm that operates using angular distances rather than traditional Euclidean metrics. This adaptation allows for a faster convergence and more accurate grouping of scattering mechanisms within the polarimetric space. Second, we will apply and evaluate this improved approach using BIOMASS mission data. The BIOMASS dataset provides a valuable test case due to its high sensitivity to vegetation and structural parameters, offering an ideal context to assess the method’s effectiveness in real-world scenarios. For instance, through this application, we aim to determine whether the proposed technique can successfully isolate and separate the surface scattering contribution from other dominant mechanisms present in the data.

[1] Polarisation: Applications in Remote Sensing, Cloude, S. R., Oxford University Press, Oxford, UK, 2009 "