Multispectral remote sensing images are often contaminated by haze, which causes low
image quality. In this paper, a novel dehazing method based on a deep convolutional neural …

In many remote sensing (RS) applications, haze seriously degrades the quality of optical RS
images and even brings inconvenience to the following high-level visual tasks such as RS …

Image visibility is affected by the presence of haze, fog, smoke, aerosol, etc. Image dehazing
using either single visible image or visible and near-infrared (NIR) image pair is often …

Satellite remote sensing image often suffers from haze degradation, which deteriorates
significantly the effect of data intelligibility and interpretability. Hence, haze removal …

Images obtained under adverse weather conditions, such as haze or fog, typically exhibit
low contrast and faded colors, which may severely limit the visibility within the scene …

Multispectral remote sensing images are often degraded by clouds, resulting in the reduced
efficiency and accuracy of image interpretation. Thin cloud removal is one of the most …

Existing thin cloud removal methods treat this image restoration task as a point estimation
problem, and produce a single cloud-free image following a deterministic pipeline. In this …

Haze contamination is a common issue in optical remote sensing images, including
hyperspectral images (HSIs), which can distort the spectral features of land cover objects …

Artisanal gold mining (galamsey) and cocoa farming are essential sources of income for
local populations in Ghana. Unfortunately the former poses serious threats to the …

The objective of any radiometric correction of airborne and spaceborne imagery of optical
sensors is the extraction of physical earth surface parameters such as spectral albedo …