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

Xiao Xiang Zhu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Xiao Xiang Zhu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 691 publications — 98th percentile

98% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Xiao Xiang Zhu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Xiao Xiang Zhu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 77 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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