World's Best Scientists 2026 revealed!
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Computer Science
Germany
2025

D-Index & Metrics

Computer Science

D-Index
77
Citations
24672
World Ranking
1266
National Ranking
50

Research.com Recognitions

  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award
  • 2021 - IEEE Fellow For contributions to artificial intelligence and data science in Earth observation and global urban mapping

Overview

Xiao Xiang Zhu is affiliated with the Technical University of Munich in Germany. Their research spans multiple areas within computer science and engineering, with a strong focus on remote sensing and artificial intelligence.

Their recent publications highlight a diverse engagement with topics related to deep learning, remote sensing imagery, and data fusion techniques. Notable works include:

  • A survey of uncertainty in deep neural networks, 2023, Artificial Intelligence Review
  • Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks, 2023, Remote Sensing of Environment
  • Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion, 2020, ISPRS Journal of Photogrammetry and Remote Sensing
  • Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification, 2020, IEEE Transactions on Geoscience and Remote Sensing
  • Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives, 2021, IEEE Geoscience and Remote Sensing Magazine

Xiao Xiang Zhu has collaborated frequently with several researchers, including:

  • Lichao Mou
  • Yilei Shi
  • Sudipan Saha
  • Zhitong Xiong
  • Björn Lütjens

They have published extensively in prominent venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Transactions on Geoscience and Remote Sensing
  • IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
  • ISPRS Journal of Photogrammetry and Remote Sensing

Their main fields of study are computer science and engineering, with a specialization in various subfields including:

  • Computer Vision and Pattern Recognition
  • Media Technology
  • Artificial Intelligence
  • Atmospheric Science
  • Global and Planetary Change

The primary research topics covered by their work include:

  • Remote-Sensing Image Classification
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Remote Sensing in Agriculture
  • Remote Sensing and LiDAR Applications
  • Remote Sensing and Land Use
  • Advanced Image Fusion Techniques

In 2021, Xiao Xiang Zhu was recognized as an IEEE Fellow for contributions to artificial intelligence and data science in Earth observation and global urban mapping.

Best Publications

  • Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

    Xiao Xiang Zhu;Devis Tuia;Lichao Mou;Gui-Song Xia

  • Deep learning in remote sensing: a review

    Xiao Xiang Zhu;Devis Tuia;Lichao Mou;Gui-Song Xia

  • Deep Recurrent Neural Networks for Hyperspectral Image Classification

    Unknown

  • A Survey of Uncertainty in Deep Neural Networks.

    Jakob Gawlikowski;Cedrique Rovile Njieutcheu Tassi;Mohsin Ali;Jongseok Lee

  • An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

    Danfeng Hong;Naoto Yokoya;Jocelyn Chanussot;Xiao Xiang Zhu

  • Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach

    Xiao Xiang Zhu;Richard Bamler

  • Very High Resolution Spaceborne SAR Tomography in Urban Environment

    Xiao Xiang Zhu;Richard Bamler

  • A Sparse Image Fusion Algorithm With Application to Pan-Sharpening

    Xiao Xiang Zhu;Richard Bamler

  • Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks

    Unknown

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

    Andrea Meraner;Patrick Ebel;Xiao Xiang Zhu;Xiao Xiang Zhu;Michael Schmitt

  • Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

    Lichao Mou;Lorenzo Bruzzone;Xiao Xiang Zhu

  • Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic SAR

    Xiao Xiang Zhu;Richard Bamler

  • Building instance classification using street view images

    Jian Kang;Marco Körner;Yuanyuan Wang;Hannes Taubenböck

  • Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation

    Yuxing Xie;Jiaojiao Tian;Xiao Xiang Zhu

  • Self-Supervised Learning in Remote Sensing: A review

    Unknown

  • Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks

    Michael Wurm;Thomas Stark;Xiao Xiang Zhu;Xiao Xiang Zhu;Matthias Weigand;Matthias Weigand

  • SEN12MS – A CURATED DATASET OF GEOREFERENCED MULTI-SPECTRAL SENTINEL-1/2 IMAGERY FOR DEEP LEARNING AND DATA FUSION

    Michael Schmitt;Lloyd Haydn Hughes;Chunping Qiu;Xiao Xiang Zhu;Xiao Xiang Zhu

  • Data Fusion and Remote Sensing: An ever-growing relationship

    Michael Schmitt;Xiao Xiang Zhu

  • Unsupervised Spectral–Spatial Feature Learning via Deep Residual Conv–Deconv Network for Hyperspectral Image Classification

    Lichao Mou;Pedram Ghamisi;Xiao Xiang Zhu

  • Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives

    Xiaoxiang Zhu;Sina Montazeri;Mohsin Ali;Yuansheng Hua

  • THE SEN1-2 DATASET FOR DEEP LEARNING IN SAR-OPTICAL DATA FUSION

    Michael Schmitt;Lloyd Haydn Hughes;Xiao Xiang Zhu;Xiao Xiang Zhu

  • Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model.

    Danfeng Hong;Jingliang Hu;Jing Yao;Jocelyn Chanussot;Jocelyn Chanussot

  • Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification

    Danfeng Hong;Naoto Yokoya;Nan Ge;Jocelyn Chanussot

  • Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification

    Lichao Mou;Xiaoqiang Lu;Xuelong Li;Xiao Xiang Zhu

  • Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network

    Pedram Ghamisi;Bernhard Hofle;Xiao Xiang Zhu

  • Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

    Lichao Mou;Lorenzo Bruzzone;Xiao Xiang Zhu

  • Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

    Danfeng Hong;Xin Wu;Pedram Ghamisi;Jocelyn Chanussot

  • A Review of Point Cloud Semantic Segmentation

    Yuxing Xie;Jiaojiao Tian;Xiao Xiang Zhu

Frequent Co-Authors

Richard Bamler
Richard Bamler German Aerospace Center
Lichao Mou
Lichao Mou Technical University of Munich
Danfeng Hong
Danfeng Hong Chinese Academy of Sciences
Jocelyn Chanussot
Jocelyn Chanussot Grenoble Alpes University
Naoto Yokoya
Naoto Yokoya University of Tokyo
Jian Kang
Jian Kang University College London
Pedram Ghamisi
Pedram Ghamisi Helmholtz-Zentrum Dresden-Rossendorf
Hannes Taubenböck
Hannes Taubenböck German Aerospace Center
Michael Eineder
Michael Eineder German Aerospace Center
Peter Reinartz
Peter Reinartz German Aerospace Center

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