Multispectral remote sensing (RS) images are often contaminated by the haze that degrades
the quality of RS data and reduces the accuracy of interpretation and classification …

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 …

Remote sensing image dehazing (RSID) is an extremely challenging problem due to the
irregular and nonuniform distribution of haze. The existing RSID methods achieve excellent …

Remote sensing (RS) dehazing is a challenging task since various haze distributions
severely degrade the image quality. Recent learning-based methods achieve dramatic …

Existing dehazing algorithms are not effective for remote sensing images (RSIs) with dense
haze, and dehazed results are prone to over-enhancement, color distortion, and artifacts. To …

Remote sensing image dehazing is an extremely complex issue due to the irregular and non-
uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network …

Satellite image dehazing aims at precisely retrieving the real situations of the obscured parts
from the hazy remote sensing (RS) images, which is a challenging task since the hazy …

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 …

Remote sensing (RS) image dehazing is an effective means to enhance the quality of hazy
RS images. However, existing dehazing methods are ineffective in dealing with …

The haze in remote sensing images can cause the decline of image quality and bring many
obstacles to the applications of remote sensing images. Considering the non-uniform …