Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration.
Speckle filtering is generally a prerequisite to the analysis of synthetic aperture radar (SAR) images.
Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data.
We introduce a self-supervised strategy based on the separation of the real and imaginary parts of single-look complex SAR images, called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a straightforward way to train all kinds of deep despeckling networks.
Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community.
This segmentation process can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces.
The proposed method combines this multi-temporal average and the image at a given date in the form of a ratio image and uses a state-of-the-art neural network to remove the speckle in this ratio image.
This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy.
Many different schemes have been proposed for the restoration of intensity SAR images.
A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters.
Image restoration methods aim to recover the underlying clean image from corrupted observations.