196 citations · 504 across the 7 of their papers we have counts for
8 papers
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Patrick Ebel, Vivien Sainte Fare Garnot, Michael Schmitt +2
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to…
Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark
Fang Xu, Yilei Shi, Patrick Ebel +2
Cloud removal is a significant and challenging problem in remote sensing, and in recent years, there have been notable advancements in this area. However, two major issues remain h…
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
Fang Xu, Yilei Shi, Patrick Ebel +4
The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap betwee…
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal
Patrick Ebel, Yajin Xu, Michael Schmitt +1
About half of all optical observations collected via spaceborne satellites are affected by haze or clouds. Consequently, cloud coverage affects the remote sensing practitioner's ca…
Self-Supervised Multisensor Change Detection
Sudipan Saha, Patrick Ebel, Xiao Xiang Zhu
Most change detection methods assume that pre-change and post-change images are acquired by the same sensor. However, in many real-life scenarios, e.g., natural disaster, it is mor…
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
Patrick Ebel, Andrea Meraner, Michael Schmitt +1
This work has been accepted by IEEE TGRS for publication. The majority of optical observations acquired via spaceborne earth imagery are affected by clouds. While there is numerous…