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 …

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 …

Image restoration is a long-standing problem in low-level computer vision with many
interesting applications. We describe a flexible learning framework based on the concept of …

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 …

Change detection of heterogeneous remote sensing images is an important and challenging
topic, which has found a wide range of applications in many fields, especially in the …

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 …

Automatic and high-precision extraction of buildings from remote sensing images has a wide
range of application and importance. Optical and synthetic aperture radar (SAR) images are …

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 …

In this article, we propose an automatic and efficient method to solve optical and synthetic
aperture radar (SAR) image registration using the improved phase congruency (PC) model …

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 …