World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
7237
World Ranking
10593
National Ranking
664

Stephen Marshall 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 Stephen Marshall 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: 261 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Stephen Marshall 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 Stephen Marshall 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Stephen Marshall is affiliated with the University of Strathclyde in the United Kingdom. Their research spans multiple areas within engineering and computer science, with a particular focus on computer vision, pattern recognition, and media technology. The subfields they contribute to include artificial intelligence, industrial and manufacturing engineering, and biomedical engineering.

The scientist's work extensively covers topics such as remote-sensing image classification, advanced image fusion techniques, image and signal denoising methods, advanced image and video retrieval techniques, industrial vision systems and defect detection, advanced steganography and watermarking techniques, and geochemistry and geologic mapping.

Some of Stephen Marshall's recent papers are:

  • A framework for breast cancer classification using Multi-DCNNs, 2021, Computers in Biology and Medicine
  • Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images, 2020, IEEE Access
  • Tri-CNN: A Three Branch Model for Hyperspectral Image Classification, 2023, Remote Sensing
  • A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques, 2023, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Achieving remanufacturing inspection using deep learning, 2020, Journal of remanufacturing

Frequent coauthors collaborating with Stephen Marshall include:

  • Paul Murray
  • Jinchang Ren
  • Jaime Zabalza
  • Huimin Zhao
  • Nour Aburaed

Stephen Marshall often publishes in the following venues:

  • arXiv (Cornell University)
  • Remote Sensing
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • IEEE Access
  • Scientific Reports

In addition to journal articles and conference papers, Stephen Marshall has contributed to book publications. Notably, they have published a book titled Communications, Signal Processing, and Systems in 2022 under Springer Science+Business Media.

Best Publications

  • Activation functions: comparison of trends in practice and research for deep learning

    Chigozie Enyinna Nwankpa;Winifred Ijomah;Anthony Gachagan;Stephen Marshall

  • Breast cancer detection using deep convolutional neural networks and support vector machines

    Dina A. Ragab;Dina A. Ragab;Maha Sharkas;Stephen Marshall;Jinchang Ren

  • Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

    Jaime Zabalza;Jinchang Ren;Jiangbin Zheng;Huimin Zhao

  • Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

    Jaime Zabalza;Jinchang Ren;Mingqiang Yang;Yi Zhang

  • Convergence Criteria for Genetic Algorithms

    David Greenhalgh;Stephen Marshall

  • Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging

    Jaime Zabalza;Jinchang Ren;Jiangbin Zheng;Junwei Han

  • A framework for breast cancer classification using Multi-DCNNs.

    Dina Ahmed Ragab;Dina Ahmed Ragab;Omneya Attallah;Maha A. Sharkas;Jinchang Ren

  • Multiresolution morphological fusion of MR and CT images of the human brain

    G.K. Matsopoulos;S. Marshall;J.N.H. Brunt

  • Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement

    Yijun Yan;Jinchang Ren;Genyun Sun;Huimin Zhao

  • Effective Denoising and Classification of Hyperspectral Images Using Curvelet Transform and Singular Spectrum Analysis

    Tong Qiao;Jinchang Ren;Zheng Wang;Jaime Zabalza

  • New methods for automatic quantification of microstructural features using digital image processing

    Andrew Campbell;Paul Murray;Evgenia Yakushina;Stephen Marshall

  • Hyperspectral imaging combined with data classification techniques as an aid for artwork authentication

    Adam Polak;Timothy Kelman;Paul Murray;Stephen Marshall

  • Singular Spectrum Analysis for Effective Feature Extraction in Hyperspectral Imaging

    Jaime Zabalza;Jinchang Ren;Zheng Wang;Stephen Marshall

  • Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging [Applications Corner]

    Jianchang Ren;Jaime Zabalza;Stephen Marshall;Jiangbin Zheng

  • Tri-CNN: A Three Branch Model for Hyperspectral Image Classification

    Unknown

  • Robust PCA micro-doppler classification using SVM on embedded systems

    Jaime Zabalza;Carmine Clemente;Gaetano Di Caterina;Jinchang Ren

  • Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images

    Samson Damilola Fabiyi;Hai Vu;Christos Tachtatzis;Paul Murray

  • The use of genetic algorithms in morphological filter design

    Neal R. Harvey;Stephen Marshall

  • Cognitive Fusion of Thermal and Visible Imagery for Effective Detection and Tracking of Pedestrians in Videos

    Yijun Yan;Jinchang Ren;Huimin Zhao;Genyun Sun

  • Logic models of pathway biology.

    Steven Watterson;Stephen Marshall;Peter Ghazal

  • Genetic algorithm optimization of multidimensional grayscale soft morphological filters with applications in film archive restoration

    M.S. Hamid;N.R. Harvey;S. Marshall

Frequent Co-Authors

Jinchang Ren
Jinchang Ren Robert Gordon University
Edward R. Dougherty
Edward R. Dougherty Texas A&M University
Ivan Andonovic
Ivan Andonovic University of Strathclyde
Peter Ghazal
Peter Ghazal Cardiff University
Stephen McArthur
Stephen McArthur University of Strathclyde
Junwei Han
Junwei Han Northwestern Polytechnical University
Jon Atli Benediktsson
Jon Atli Benediktsson University of Iceland
Shutao Li
Shutao Li Hunan University
Peijun Du
Peijun Du Nanjing University
Xuelong Li
Xuelong Li China Telecom (China)

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