Spectral Clustering of Polarimetric SAR Data with Wishart Derived Distance Measure

This paper presents a new spectral clustering algorithm,
which is specially tailored for segmentation of polarimetric
SAR images. This is accomplished by use of
certain pairwise distance measures between pixels. The
measures are derived from the complex Wishart distribution,
and capture the statistical information contained in
the coherency matrix. We demonstrate how the pairwise
distances are transformed into an affinity matrix, whose
eigendecomposition determines the optimal partitioning
of pixels. We further show that the obtained clustering
provides an improved initialization of the classical unsupervised Wishart classifier, and that the entire classification can also be performed in a kernel induced feature space. The algorithms are tested on crop classification with promising results.