Image restoration for remote sensing: Overview and toolbox
Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
[HTML][HTML] Polarimetric imaging via deep learning: A review
Polarization can provide information largely uncorrelated with the spectrum and intensity.
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …
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 …
Explainable, physics-aware, trustworthy artificial intelligence: A paradigm shift for synthetic aperture radar
The recognition or understanding of the scenes observed with a synthetic aperture radar
(SAR) system requires a broader range of cues beyond the spatial context. These …
(SAR) system requires a broader range of cues beyond the spatial context. These …
SAR2SAR: A semi-supervised despeckling algorithm for SAR images
Speckle reduction is a key step in many remote sensing applications. By strongly affecting
synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty …
synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty …
Speckle2Void: Deep self-supervised SAR despeckling with blind-spot convolutional neural networks
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by
speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis …
speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis …
As if by magic: Self-supervised training of deep despeckling networks with MERLIN
Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR)
images. Speckle reduction has thus been the subject of numerous works spanning at least …
images. Speckle reduction has thus been the subject of numerous works spanning at least …
Multi-objective CNN-based algorithm for SAR despeckling
Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is
largely used in applications, such as change detection, image restoration, segmentation …
largely used in applications, such as change detection, image restoration, segmentation …
A survey on the applications of convolutional neural networks for synthetic aperture radar: Recent advances
In recent years, convolutional neural networks (CNNs) have drawn considerable attention
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …
SAR despeckling using multiobjective 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 …