  A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect
images in different polarization states, making it a rich source of information
for target characterization. PolSAR images are inherently affected by speckle.
Therefore, before deriving ad hoc products from the data, the polarimetric
covariance matrix needs to be estimated by reducing speckle. In recent years,
deep learning based despeckling methods have started to evolve from single
channel SAR images to PolSAR images. To this aim, deep learning based
approaches separate the real and imaginary components of the complex-valued
covariance matrix and use them as independent channels in a standard
convolutional neural networks. However, this approach neglects the mathematical
relationship that exists between the real and imaginary components, resulting
in sub-optimal output. Here, we propose a multi-stream complex-valued fully
convolutional network to reduce speckle and effectively estimate the PolSAR
covariance matrix. To evaluate the performance of CV-deSpeckNet, we used
Sentinel-1 dual polarimetric SAR images to compare against its real-valued
counterpart, that separates the real and imaginary parts of the complex
covariance matrix. CV-deSpeckNet was also compared against the state of the art
PolSAR despeckling methods. The results show CV-deSpeckNet was able to be
trained with a fewer number of samples, has a higher generalization capability
and resulted in a higher accuracy than its real-valued counterpart and
state-of-the-art PolSAR despeckling methods. These results showcase the
potential of complex-valued deep learning for PolSAR despeckling.
