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 …

Speckle noise is an inherent problem in coherent imaging systems such as synthetic
aperture radar. It creates strong intensity fluctuations and hampers the analysis of images …

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 …

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since
most current and planned SAR imaging satellites operate in polarimetric, interferometric, or …

In this letter, a new superpixel-based constant-false-alarm-rate (CFAR) target detection
algorithm for high-resolution synthetic aperture radar (SAR) images is proposed. 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 …

Traditionally, clean reference images are needed to train the networks when applying the
deep learning techniques to tackle image denoising tasks. However, this idea is …

We propose a new local descriptor for fingerprint liveness detection. The input image is
analyzed both in the spatial and in the frequency domain, in order to extract information on …

Synthetic aperture radar (SAR) images are inherently affected by speckle noise, for which
deep learning-based methods have shown good potential. However, the deep learning …