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Found 13 papers, 6 papers with code
Patch-based adaptive temporal filter and residual evaluation
Spatial adaptive denoising methods can improve the patch-based weighted temporal average image when the time series is limited.
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.
Unrolling PALM for sparse semi-blind source separation
Sparse Blind Source Separation (BSS) has become a well established tool for a wide range of applications - for instance, in astrophysics and remote sensing.
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.
Multi-View Radar Semantic Segmentation
Understanding the scene around the ego-vehicle is key to assisted and autonomous driving.
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.
