Training supervised neural networks for PolSAR despeckling with an hybrid approach

Synthetic aperture radar (SAR) are fundamental
system for Earth Observation. In particular, polarimetric SAR
(PolSAR) sensors provide images of a scene at different polarizations, enriching the information that can be retrieved. Due to
their coherent nature, SAR images are complex data affected by
a multiplicative noise, called speckle. The presence of this noise
hinders the interpretation of images, making speckle removal a
fundamental preprocessing step for further applications. Several
deep learning (DL)-based approaches have been recently proposed for speckle removal in PolSAR data relying, due to the lack
of a real ground truth, on different strategies for constructing
training datasets making a real comparison complicated. In this
work, a study on the construction of a training dataset for
PolSAR despeckling is proposed. In particular, considering the
analysis recently conducted on the construction of the dataset
for training supervised neural networks for SAR amplitude
despeckling, the aim is to extend such studies to the PolSAR
case. In particular, the commonly used multitemporal approach,
relying on the stack of real data, is compared with the socalled hybrid approach, in which a mixture of real and synthetic
data is proposed. A specific DL solution has been chosen
for such comparison, but the analysis could be extended to
whatever supervised neural network. Moreover, for the sake
of completeness, results are also compared with a well-assessed
PolSAR despeckling filter in the literature.