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Lichao Mou
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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
45
Citations
12430
World Ranking
436
National Ranking
12

Computer Science

D-Index
46
Citations
9311
World Ranking
6815
National Ranking
325

Lichao Mou 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 Lichao Mou 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: 174 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Lichao Mou 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 Lichao Mou 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: 46 D-Index — 53rd percentile

53% 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 Rising Stars Award

Overview

Lichao Mou is affiliated with the Technical University of Munich in Germany. Their research focuses primarily on computer science and engineering, with significant contributions in subfields including computer vision and pattern recognition, media technology, artificial intelligence, atmospheric science, and aerospace engineering.

The research topics covered by Lichao Mou include:

  • Remote-Sensing Image Classification
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Video Surveillance and Tracking Methods
  • Multimodal Machine Learning Applications
  • Automated Road and Building Extraction
  • Advanced Neural Network Applications

Lichao Mou has published extensively in various scientific venues. The most frequent publication venues are:

  • IEEE Transactions on Geoscience and Remote Sensing
  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • International Journal of Applied Earth Observation and Geoinformation
  • IEEE Geoscience and Remote Sensing Magazine

Some of their recent papers include:

  • Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification, 2020, IEEE Transactions on Geoscience and Remote Sensing
  • Relation Matters: Relational Context-Aware Fully Convolutional Network for Semantic Segmentation of High-Resolution Aerial Images, 2020, IEEE Transactions on Geoscience and Remote Sensing

Other notable papers relevant to their field of study, though authored by collaborators, include:

  • Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives, 2021, IEEE Geoscience and Remote Sensing Magazine
  • Self-Supervised Learning in Remote Sensing: A review, 2022, IEEE Geoscience and Remote Sensing Magazine
  • Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images, 2022, IEEE Transactions on Geoscience and Remote Sensing

Lichao Mou frequently collaborates with a number of researchers, including:

  • Xiao Xiang Zhu
  • Yuansheng Hua
  • Yilei Shi
  • Konrad Heidler
  • Jingliang Hu

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

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

    Lichao Mou;Lorenzo Bruzzone;Xiao Xiang Zhu

  • Self-Supervised Learning in Remote Sensing: A review

    Unknown

  • Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

    Haobo Lyu;Hui Lu;Lichao Mou

  • 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

  • Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification

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

  • Identifying Corresponding Patches in SAR and Optical Images With a Pseudo-Siamese CNN

    Lloyd H. Hughes;Michael Schmitt;Lichao Mou;Yuanyuan Wang

  • Scene Recognition by Manifold Regularized Deep Learning Architecture

    Yuan Yuan;Lichao Mou;Xiaoqiang Lu

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

    Lichao Mou;Lorenzo Bruzzone;Xiao Xiang Zhu

  • Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification

    Lichao Mou;Xiao Xiang Zhu

  • HSF-Net: Multiscale Deep Feature Embedding for Ship Detection in Optical Remote Sensing Imagery

    Qingpeng Li;Lichao Mou;Qingjie Liu;Yunhong Wang

  • A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes

    Lichao Mou;Yuansheng Hua;Xiao Xiang Zhu

  • Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images

    Lei Ding;Haitao Guo;Sicong Liu;Lichao Mou

  • Semi-Supervised Multitask Learning for Scene Recognition

    Xiaoqiang Lu;Xuelong Li;Lichao Mou

  • Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification.

    Yuansheng Hua;Yuansheng Hua;Lichao Mou;Lichao Mou;Xiao Xiang Zhu;Xiao Xiang Zhu

  • Relation Matters: Relational Context-Aware Fully Convolutional Network for Semantic Segmentation of High-Resolution Aerial Images

    Lichao Mou;Yuansheng Hua;Xiao Xiang Zhu

  • HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

    Konrad Heidler;Lichao Mou;Celia Baumhoer;Andreas Dietz

  • Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network

    Lichao Mou;Xiao Xiang Zhu

  • Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network.

    Chunping Qiu;Lichao Mou;Lichao Mou;Michael Schmitt;Xiao Xiang Zhu;Xiao Xiang Zhu

  • HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

    Konrad Heidler;Lichao Mou;Celia A. Baumhoer;Andreas J. Dietz

  • An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning

    Xu Tang;Huayu Zhang;Lichao Mou;Fang Liu

  • So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]

    Xiao Xiang Zhu;Jingliang Hu;Chunping Qiu;Yilei Shi

  • IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network.

    Lichao Mou;Xiao Xiang Zhu

Frequent Co-Authors

Xiao Xiang Zhu
Xiao Xiang Zhu Technical University of Munich
Lorenzo Bruzzone
Lorenzo Bruzzone University of Trento
Xiaoqiang Lu
Xiaoqiang Lu Chinese Academy of Sciences
Devis Tuia
Devis Tuia École Polytechnique Fédérale de Lausanne
Francesca Bovolo
Francesca Bovolo Fondazione Bruno Kessler
Pedram Ghamisi
Pedram Ghamisi Helmholtz-Zentrum Dresden-Rossendorf
Xuelong Li
Xuelong Li China Telecom (China)
Feng Xu
Feng Xu Fudan University
Z. Jane Wang
Z. Jane Wang University of British Columbia
Hui Lu
Hui Lu Tsinghua University

External Links

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