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 …
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 …
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 …
Structure consistency-based graph for unsupervised change detection with homogeneous and heterogeneous remote sensing images
Change detection (CD) of remote sensing (RS) images is one of the important problems in
earth observation, which has been extensively studied in recent years. However, with the …
earth observation, which has been extensively studied in recent years. However, with the …
SAR image despeckling through convolutional neural networks
In this paper we investigate the use of discriminative model learning through Convolutional
Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning …
Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning …
Automated extraction of surface water extent from Sentinel-1 data
Accurately quantifying surface water extent in wetlands is critical to understanding their role
in ecosystem processes. However, current regional-to global-scale surface water products …
in ecosystem processes. However, current regional-to global-scale surface water products …
Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in …
InSAR technology provides a powerful tool to detect potentially unstable slopes across wide
areas and to monitor surface displacements of a single landslide. However, conventional …
areas and to monitor surface displacements of a single landslide. However, conventional …
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 …
Sar-to-optical image translation based on conditional generative adversarial networks—Optimization, opportunities and limits
Due to its all time capability, synthetic aperture radar (SAR) remote sensing plays an
important role in Earth observation. The ability to interpret the data is limited, even for …
important role in Earth observation. The ability to interpret the data is limited, even for …
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 …