Synthetic aperture radar (SAR) has drawn significant interest from the remote sensing
community due to its ability to provide high-resolution images irrespective of weather and …

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 …

In this paper, to break the limit of the traditional linear models for synthetic aperture radar
(SAR) image despeckling, we propose a novel deep learning approach by learning a non …

Remote Sensing Image Fusion: A Practical Guide gives an introduction to remote sensing
image fusion providing an overview on the sensors and applications. It describes data …

SAR (Synthetic Aperture Radar) imaging plays a central role in Remote Sensing due to,
among other important features, its ability to provide high-resolution, day-and-night and …

We propose a despeckling algorithm for multitemporal synthetic aperture radar (SAR)
images based on the concepts of block-matching and collaborative filtering. It relies on the …

Satellite images of synthetic aperture radar (SAR) sensors are contaminated by speckles
from the coherent imaging mechanism. Although removing or mitigating speckle has been a …

SAR images are inherently affected by speckle noise, and although attempts made earlier to
remove speckle succeeded, there is still the challenge of preserving the edges of images …

The near-real-time detection of selective logging in tropical forests is essential to support
actions for reducing CO2 emissions and for monitoring timber extraction from forest …

Among the large number of synthetic aperture radar (SAR) image despeckling approaches
existing in literature, nonlocal (NL) filters have received a desirable boost. However, often …