Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate,
well-documented, and automated cloud detection algorithms are necessary to effectively …

Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because
in-situ monitoring of mangroves is difficult and time-consuming, remote sensing …

We developed the Function of mask (Fmask) 4.0 algorithm for automated cloud and cloud
shadow detection in Landsats 4–8 and Sentinel-2 images. Three major innovative …

Urban areas and their vertical characteristics have a manifold and far-reaching impact on
our environment. However, openly accessible information at high spatial resolution is still …

Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready
data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper …

Reliable identification of clouds is necessary for any type of optical remote sensing image
analysis, especially in operational and fully automatic setups. One of the most elaborated …

This paper formulates cloud and cloud shadow detection as a semantic segmentation
problem and proposes a deep convolutional neural network (CNN) based method to detect …

The exploitation of Earth observation satellite images acquired by optical instruments
requires an automatic and accurate cloud detection. Multitemporal approaches to cloud …

Cloud detection is a crucial step in the optical satellite image processing pipeline for Earth
observation. Clouds in optical remote sensing images seriously affect the visibility of the …

Formulated as a pixel-level labeling task, data-driven neural segmentation models for cloud
and corresponding shadow detection have achieved a promising accomplishment in remote …