Patrick Ebel

About Patrick Ebel

Patrick Ebel, With an exceptional h-index of 9 and a recent h-index of 9 (since 2020), a distinguished researcher at Technische Universität München, specializes in the field of Computer Vision, Remote Sensing, Machine Learning, Image Reconstruction.

His recent articles reflect a diverse array of research interests and contributions to the field:

Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting

Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark

Deep Learning for Cloud Removal in Spaceborne Earth Observation

UnCRtainTS: Uncertainty quantification for cloud removal in optical satellite time series

Supervised change detection using prechange optical-SAR and postchange SAR data

Exploring the Potential of SAR Data for Cloud Removal in Optical Satellite Imagery

SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal

Explicit Haze & Cloud Removal for Global Land Cover Classification

Patrick Ebel Information

University

Position

___

Citations(all)

656

Citations(since 2020)

650

Cited By

80

hIndex(all)

9

hIndex(since 2020)

9

i10Index(all)

9

i10Index(since 2020)

9

Email

University Profile Page

Google Scholar

Patrick Ebel Skills & Research Interests

Computer Vision

Remote Sensing

Machine Learning

Image Reconstruction

Top articles of Patrick Ebel

Title

Journal

Author(s)

Publication Date

Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting

arXiv preprint arXiv:2404.05758

Patrick Ebel

Brandon Victor

Peter Naylor

Gabriele Meoni

Federico Serva

...

2024/4/5

Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark

IEEE Transactions on Geoscience and Remote Sensing

Fang Xu

Yilei Shi

Patrick Ebel

Wen Yang

Xiao Xiang Zhu

2023/12/12

Deep Learning for Cloud Removal in Spaceborne Earth Observation

Patrick Ebel

2023

UnCRtainTS: Uncertainty quantification for cloud removal in optical satellite time series

Patrick Ebel

Vivien Sainte Fare Garnot

Michael Schmitt

Jan Dirk Wegner

Xiao Xiang Zhu

2023

Supervised change detection using prechange optical-SAR and postchange SAR data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Sudipan Saha

Muhammad Shahzad

Patrick Ebel

Xiao Xiang Zhu

2022/9/23

Exploring the Potential of SAR Data for Cloud Removal in Optical Satellite Imagery

arXiv e-prints

Fang Xu

Yilei Shi

Patrick Ebel

Lei Yu

Gui-Song Xia

...

2022/6

SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal

IEEE Transactions on Geoscience and Remote Sensing

Patrick Ebel

Yajin Xu

Michael Schmitt

Xiao Xiang Zhu

2022/1/24

Explicit Haze & Cloud Removal for Global Land Cover Classification

Ziqi Gu

Patrick Ebel

Qiangqiang Yuan

Michael Schmitt

Xiao Xiang Zhu

2022

Explaining the effects of clouds on remote sensing scene classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Jakob Gawlikowski

Patrick Ebel

Michael Schmitt

Xiao Xiang Zhu

2022/11/21

GLF-CR: SAR-enhanced cloud removal with global–local fusion

ISPRS Journal of Photogrammetry and Remote Sensing

Fang Xu

Yilei Shi

Patrick Ebel

Lei Yu

Gui-Song Xia

...

2022/10/1

Fusing multi-modal data for supervised change detection

The international archives of the photogrammetry, remote sensing and spatial information sciences

Patrick Ebel

Sudipan Saha

Xiao Xiang Zhu

2021/6/28

Multisensor data fusion for cloud removal in global and all-season Sentinel-2 imagery

IEEE Transactions on Geoscience and Remote Sensing

Patrick Ebel

Andrea Meraner

Michael Schmitt

Xiao Xiang Zhu

2020/10/2

Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion

ISPRS Journal of Photogrammetry and Remote Sensing

Andrea Meraner

Patrick Ebel

Xiao Xiang Zhu

Michael Schmitt

2020/8/1

Weakly supervised semantic segmentation of satellite images for land cover mapping--challenges and opportunities

Michael Schmitt

Jonathan Prexl

Patrick Ebel

Lukas Liebel

Xiao Xiang Zhu

2020/2/19

See List of Professors in Patrick Ebel University(Technische Universität München)

Co-Authors

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