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Found 13 papers, 4 papers with code
Just Project! Multi-Channel Despeckling, the Easy Way
Reducing speckle fluctuations in multi-channel SAR images is essential in many applications of SAR imaging such as polarimetric classification or interferometric height estimation.
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR
In addition, we apply the simplified generalized likelihood ratio to detect the maximum change magnitude time, and the change starting and ending times.
A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas
Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration.
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD 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.
Multi-temporal speckle reduction with self-supervised deep neural networks
Speckle filtering is generally a prerequisite to the analysis of synthetic aperture radar (SAR) images.
As if by magic: self-supervised training of deep despeckling networks with MERLIN
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.
Image Restoration for Remote Sensing: Overview and Toolbox
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.
Urban Surface Reconstruction in SAR Tomography by Graph-Cuts
This segmentation process can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces.
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation
This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy.
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning
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.
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
Many different schemes have been proposed for the restoration of intensity SAR images.
SAR2SAR: a semi-supervised despeckling algorithm for 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.
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\times$ speed-up
Image restoration methods aim to recover the underlying clean image from corrupted observations.
