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Rising Stars

D-Index
60
Citations
14399
World Ranking
159
National Ranking
52

Computer Science

D-Index
68
Citations
21706
World Ranking
2061
National Ranking
285

Danfeng Hong 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 Danfeng Hong 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: 303 publications — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Danfeng Hong 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 Danfeng Hong 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: 68 D-Index — 86th percentile

86% 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

Danfeng Hong is affiliated with the Chinese Academy of Sciences in China. Their research primarily focuses on the intersection of engineering and computer science, with a significant emphasis on media technology and computer vision and pattern recognition. They actively contribute to advancements in the fields of atmospheric science, ecology, and artificial intelligence as well.

The scientist's work covers a range of topics central to remote sensing and image analysis. These include:

  • Remote-Sensing Image Classification
  • Advanced Image Fusion Techniques
  • Remote Sensing and Land Use
  • Advanced Image and Video Retrieval Techniques
  • Remote Sensing in Agriculture
  • Image and Signal Denoising Methods
  • Image Retrieval and Classification Techniques

Danfeng Hong has published extensively, with notable frequent venues including:

  • IEEE Transactions on Geoscience and Remote Sensing
  • arXiv (Cornell University)
  • IEEE Geoscience and Remote Sensing Letters
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • ISPRS Journal of Photogrammetry and Remote Sensing

Their recent papers demonstrate a focus on hyperspectral image classification, multimodal deep learning for remote-sensing imagery, and advanced model architectures incorporating transformers. Key publications include:

  • Graph Convolutional Networks for Hyperspectral Image Classification, 2020, IEEE Transactions on Geoscience and Remote Sensing
  • More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification, 2020, IEEE Transactions on Geoscience and Remote Sensing
  • SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers, 2021, arXiv (Cornell University)
  • SpectralGPT: Spectral Remote Sensing Foundation Model, 2024, IEEE Transactions on Pattern Analysis and Machine Intelligence

Danfeng Hong collaborates frequently with several researchers, indicating stable research partnerships. Their main co-authors include Jocelyn Chanussot, Bing Zhang, Jing Yao, Lianru Gao, and Naoto Yokoya.

Best Publications

  • Graph Convolutional Networks for Hyperspectral Image Classification

    Danfeng Hong;Lianru Gao;Jing Yao;Bing Zhang

  • Graph Convolutional Networks for Hyperspectral Image Classification

    Danfeng Hong;Lianru Gao;Jing Yao;Bing Zhang

  • More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

    Danfeng Hong;Lianru Gao;Naoto Yokoya;Jing Yao

  • SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers

    Danfeng Hong;Zhu Han;Jing Yao;Lianru Gao

  • More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification

    Danfeng Hong;Lianru Gao;Naoto Yokoya;Jing Yao

  • Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

    Renlong Hang;Qingshan Liu;Danfeng Hong;Pedram Ghamisi

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

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

  • UIU-Net: U-Net in U-Net for Infrared Small Object Detection

    Unknown

  • SpectralGPT: Spectral Remote Sensing Foundation Model

    Unknown

  • Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox

    Behnood Rasti;Danfeng Hong;Renlong Hang;Pedram Ghamisi

  • Deep learning in multimodal remote sensing data fusion: A comprehensive review

    Unknown

  • Multimodal Fusion Transformer for Remote Sensing Image Classification

    Unknown

  • Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

    Unknown

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

    Unknown

  • Progress and Challenges in Intelligent Remote Sensing Satellite Systems

    Unknown

  • Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

    Renlong Hang;Zhu Li;Pedram Ghamisi;Danfeng Hong

  • Convolutional Neural Networks for Multimodal Remote Sensing Data Classification

    Xin Wu;Danfeng Hong;Jocelyn Chanussot

  • Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework

    Unknown

  • Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

    Renlong Hang;Zhu Li;Pedram Ghamisi;Danfeng Hong

  • ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

    Xin Wu;Danfeng Hong;Jiaojiao Tian;Jocelyn Chanussot

  • Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification

    Unknown

  • Deep Learning for UAV-based Object Detection and Tracking: A Survey

    Xin Wu;Wei Li;Danfeng Hong;Ran Tao

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

    Danfeng Hong;Xin Wu;Pedram Ghamisi;Jocelyn Chanussot

  • 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

  • LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection

    Unknown

  • 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

  • FCCDN: Feature Constraint Network for VHR Image Change Detection

    Unknown

  • X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data

    Danfeng Hong;Danfeng Hong;Naoto Yokoya;Gui-Song Xia;Jocelyn Chanussot;Jocelyn Chanussot

  • CoSpace: Common Subspace Learning From Hyperspectral-Multispectral Correspondences

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

  • Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR Data

    Danfeng Hong;Lianru Gao;Renlong Hang;Bing Zhang

  • Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing.

    Danfeng Hong;Lianru Gao;Jing Yao;Naoto Yokoya

  • Interpretable Hyperspectral Artificial Intelligence: When nonconvex modeling meets hyperspectral remote sensing

    Danfeng Hong;Wei He;Naoto Yokoya;Jing Yao

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

    Danfeng Hong;Xin Wu;Pedram Ghamisi;Jocelyn Chanussot

  • StfNet : A Two-Stream Convolutional Neural Network for Spatiotemporal Image Fusion

    Xun Liu;Chenwei Deng;Jocelyn Chanussot;Danfeng Hong

  • A novel hierarchical approach for multispectral palmprint recognition

    Danfeng Hong;Wanquan Liu;Jian Su;Zhenkuan Pan

Frequent Co-Authors

Jocelyn Chanussot
Jocelyn Chanussot Grenoble Alpes University
Xiao Xiang Zhu
Xiao Xiang Zhu Technical University of Munich
Naoto Yokoya
Naoto Yokoya University of Tokyo
Lianru Gao
Lianru Gao Aerospace Information Research Institute
Bing Zhang
Bing Zhang Chinese Academy of Sciences
Pedram Ghamisi
Pedram Ghamisi Helmholtz-Zentrum Dresden-Rossendorf
Ran Tao
Ran Tao Beijing Institute of Technology
Antonio Plaza
Antonio Plaza University of Extremadura
Qian Du
Qian Du Mississippi State University
Qingshan Liu
Qingshan Liu Nanjing University of Information Science and Technology

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