Self-supervised learning of deep despeckling networks with MERLIN: ensuring the statistical independence of the real and imaginary parts

Due to the wide variety of sensors, with different spatial resolutions, operating frequency bands, as well as acquisition modes (Stripmap, Spotlight, TOPS...), despeckling neural networks trained on a given type of SAR images do not generalize well. By directly training on images from the sensor and acquisition mode of interest, self-supervised learning is a very appealing solution. This paper analyses the preprocessing requirements of the MERLIN strategy that assumes statistical independence of the real and imaginary parts of single-look-complex SAR images to perform the self-supervised training. Adequate spectral corrections are proposed to handle asymmetrical spectra and moving Doppler centroids.