Michael Schmitt

Professor

Bundeswehr University Munich

Germany

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Michael Schmitt is a Professor at Bundeswehr University Munich, Germany. His research focuses on remote sensing, particularly in the application of synthetic aperture radar (SAR) and hyperspectral imagery for agricultural and urban analysis. Recent publications include work on deep learning approaches for SAR image processing and biomass estimation using geospatial models.

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2025

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2024

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2024

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2023

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2023

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2022

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2022

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2022

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2022

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2022

Collaborators (7)

Marco Körner

Deputy Head

Ludwig-Maximilians-Universität München

GERMANY

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Lloyd Hughes

EPFL (École Polytechnique Fédérale de Lausanne)

SWITZERLAND

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Ribana Roscher

Professor of Data Science for Crop Systems

Forschungszentrum Jülich

GERMANY

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Giorgia Giardina

Associate Professor

Delft University of Technology

NETHERLANDS

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Valentina Macchiarulo

Delft University of Technology

NETHERLANDS

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Gemine Vivone

National Research Council - Institute of Methodologies for Environmental Analysis

ITALY

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Yonghao Xu

Assistant Professor

Linköping University

SWEDEN

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Michael Schmitt

Research Interests

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About

Articles (20)

Comparing the relationship between NDVI and SAR backscatter across different frequency bands in agricultural areas

Geolocation-Aware Deep Coding

Synthesis of complex-valued InSAR data with a multi-task convolutional neural network

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation

IcGAN4ColSAR: A Novel Multispectral Conditional Generative Adversarial Network Approach for SAR Image Colorization

A benchmarking protocol for SAR colorization: From regression to deep learning approaches

The EEFIT Remote Sensing Reconnaissance Mission for the February 2023 Turkey Earthquakes

The SAR2Height framework for urban height map reconstruction from single SAR intensity images

Deep Occlusion Framework for Multimodal Earth Observation Data

Dense NDVI Time Series by Fusion of Optical and SAR-Derived Data

MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets]

There Are No Data Like More Data: Datasets for deep learning in Earth observation

A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset

Urban Building Classification (UBC) V2—A Benchmark for Global Building Detection and Fine-Grained Classification From Satellite Imagery

Earth Observation and Artificial Intelligence: Understanding emerging ethical issues and opportunities

The urban morphology on our planet – Global perspectives from space

Efficient ArcSAR Focusing in the Wavenumber Domain

Explaining the Effects of Clouds on Remote Sensing Scene Classification

Multiscale Feature Learning by Transformer for Building Extraction From Satellite Images

Multitask Learning for Human Settlement Extent Regression and Local Climate Zone Classification

Collaborators (7)

Marco Körner

Lloyd Hughes

Ribana Roscher

Giorgia Giardina

Valentina Macchiarulo

Gemine Vivone

Yonghao Xu

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