Deep learning methods for synthetic aperture radar image despeckling: An overview of trends and perspectives
Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-
dependent noise called speckle, which is very severe and may hinder image exploitation …
dependent noise called speckle, which is very severe and may hinder image exploitation …
Sparse synthetic aperture radar imaging from compressed sensing and machine learning: Theories, applications, and trends
Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
Deep learning meets SAR: Concepts, models, pitfalls, and perspectives
Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
MRDDANet: A multiscale residual dense dual attention network for SAR image denoising
Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle
noise, which results in the deterioration of image quality, so the removal of speckle in SAR …
noise, which results in the deterioration of image quality, so the removal of speckle in SAR …
Integration of deep learning algorithms with a Bayesian method for improved characterization of tropical deforestation frontiers using Sentinel-1 SAR imagery
Tropical deforestation frontiers continue to expand at alarming rates, but their fine-scale
temporal patterns (eg, start timing, patch forming speed, temporal clustering within a year) …
temporal patterns (eg, start timing, patch forming speed, temporal clustering within a year) …
SAR despeckling using a denoising diffusion probabilistic model
Speckle is a type of multiplicative noise that affects all coherent imaging modalities including
synthetic aperture radar (SAR) images. The presence of speckle degrades the image quality …
synthetic aperture radar (SAR) images. The presence of speckle degrades the image quality …
SAR image edge detection: review and benchmark experiments
MJ Meester, AS Baslamisli - International Journal of Remote …, 2022 - Taylor & Francis
Edges are distinct geometric features crucial to higher level object detection and recognition
in remote-sensing processing, which is a key for surveillance and gathering up-to-date …
in remote-sensing processing, which is a key for surveillance and gathering up-to-date …
SAR despeckling using multi-objective neural network trained with generic statistical samples
Synthetic aperture radar (SAR) images are impaired by the presence of speckles. Despite
the deep interest of scholars in the last decades, SAR image despeckling is still an open …
the deep interest of scholars in the last decades, SAR image despeckling is still an open …
SAR image despeckling using continuous attention module
J Ko, S Lee - IEEE Journal of Selected Topics in Applied Earth …, 2021 - ieeexplore.ieee.org
Speckle removal process is inevitable in the restoration of synthetic aperture radar (SAR)
images. Several variant methods have been proposed for enhancing SAR images over the …
images. Several variant methods have been proposed for enhancing SAR images over the …
An end-to-end deep learning approach for quantitative microwave breast imaging in real-time applications
(1) Background: In this paper, an artificial neural network approach for effective and real-
time quantitative microwave breast imaging is proposed. It proposes some numerical …
time quantitative microwave breast imaging is proposed. It proposes some numerical …