Abstract:
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In this paper,we propose the use of binary partition
trees (BPT) to introduce a novel region-based and multi-scale polarimetric
SAR (PolSAR) data representation. The BPT structure
represents homogeneous regions in the data at different detail
levels. The construction process of the BPT is based, firstly, on
a region model able to represent the homogeneous areas, and,
secondly, on a dissimilarity measure in order to identify similar
areas and define the merging sequence. Depending on the final
application, a BPT pruning strategy needs to be introduced. In this
paper, we focus on the application of BPT PolSAR data representation
for speckle noise filtering and data segmentation on the basis
of the Gaussian hypothesis, where the average covariance or coherency
matrices are considered as a region model. We introduce
and quantitatively analyze different dissimilarity measures. In this
case, and with the objective to be sensitive to the complete polarimetric
information under the Gaussian hypothesis, dissimilarity
measures considering the complete covariance or coherency matrices
are employed.When confronted to PolSAR speckle filtering,
two pruning strategies are detailed and evaluated. As presented,
the BPT PolSAR speckle filter defined filters data according to the
complete polarimetric information. As shown, this novel filtering
approach is able to achieve very strong filtering while preserving
the spatial resolution and the polarimetric information. Finally,
the BPT representation structure is employed for high spatial
resolution image segmentation applied to coastline detection. The
analyses detailed in this work are based on simulated, as well as on
real PolSAR data acquired by the ESAR system of DLR and the
RADARSAT-2 system. |