World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
8330
World Ranking
6223
National Ranking
825

Overview

Gangyao Kuang is affiliated with the National University of Defense Technology in China. Their research primarily spans the fields of Engineering and Computer Science, with notable subfields including Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology, Artificial Intelligence, and Ocean Engineering.

Their recent publications focus significantly on Synthetic Aperture Radar (SAR) imaging and related techniques.

  • BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images, 2021, Remote Sensing
  • Attention Receptive Pyramid Network for Ship Detection in SAR Images, 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • An Anchor-Free Detection Method for Ship Targets in High-Resolution SAR Images, 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Iterative Robust Graph for Unsupervised Change Detection of Heterogeneous Remote Sensing Images, 2021, IEEE Transactions on Image Processing
  • Structure Consistency-Based Graph for Unsupervised Change Detection With Homogeneous and Heterogeneous Remote Sensing Images, 2021, IEEE Transactions on Geoscience and Remote Sensing

Their main topics of work include:

  • Advanced SAR Imaging Techniques
  • Remote-Sensing Image Classification
  • Synthetic Aperture Radar (SAR) Applications and Techniques
  • Geophysical Methods and Applications
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Remote Sensing and Land Use

The frequent coauthors collaborating with Gangyao Kuang are:

  • Kefeng Ji
  • Lin Lei
  • Yuli Sun
  • Lingjun Zhao
  • Li Liu

Publication venues where Gangyao Kuang has contributed extensively include:

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

Best Publications

  • Extended local binary patterns for texture classification

    Li Liu;Lingjun Zhao;Yunli Long;Gangyao Kuang

  • An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images

    Gui Gao;Li Liu;Lingjun Zhao;Gongtao Shi

  • Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images

    Zhao Lin;Kefeng Ji;Xiangguang Leng;Gangyao Kuang

  • BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

    Zhongzhen Sun;Xiangguang Leng;Yu Lei;Boli Xiong

  • Attention Receptive Pyramid Network for Ship Detection in SAR Images

    Yan Zhao;Lingjun Zhao;Boli Xiong;Gangyao Kuang

  • An Anchor-Free Detection Method for Ship Targets in High-Resolution SAR Images

    Zhongzhen Sun;Muchen Dai;Xiangguang Leng;Yu Lei

  • SAR Target Recognition via Joint Sparse Representation of Monogenic Signal

    Ganggang Dong;Gangyao Kuang;Na Wang;Lingjun Zhao

  • Hyperspectral Image Restoration Using Low-Rank Tensor Recovery

    Haiyan Fan;Yunjin Chen;Yulan Guo;Hongyan Zhang

  • A Review of the Autoencoder and Its Variants: A Comparative Perspective from Target Recognition in Synthetic-Aperture Radar Images

    Ganggang Dong;Guisheng Liao;Hongwei Liu;Gangyao Kuang

  • Spatial–Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising

    Haiyan Fan;Chang Li;Yulan Guo;Gangyao Kuang

  • Iterative Robust Graph for Unsupervised Change Detection of Heterogeneous Remote Sensing Images

    Yuli Sun;Lin Lei;Dongdong Guan;Gangyao Kuang

  • Classification on the Monogenic Scale Space: Application to Target Recognition in SAR Image

    Ganggang Dong;Gangyao Kuang

  • Modified two-dimensional otsu image segmentation algorithm and fast realisation

    Q. Chen;L. Zhao;J. Lu;G. Kuang

  • Structure Consistency-Based Graph for Unsupervised Change Detection With Homogeneous and Heterogeneous Remote Sensing Images

    Yuli Sun;Lin Lei;Xiao Li;Xiang Tan

  • Sparse Representation of Monogenic Signal: With Application to Target Recognition in SAR Images

    Ganggang Dong;Na Wang;Gangyao Kuang

  • Nonlocal patch similarity based heterogeneous remote sensing change detection

    Yuli Sun;Lin Lei;Xiao Li;Hao Sun

  • Sorted random projections for robust rotation-invariant texture classification

    Li Liu;Paul Fieguth;David Clausi;Gangyao Kuang

  • Sorted Random Projections for robust texture classification

    Li Liu;Paul Fieguth;Gangyao Kuang;Hongbin Zha

  • Domain Knowledge Powered Two-Stream Deep Network for Few-Shot SAR Vehicle Recognition

    Linbin Zhang;Xiangguang Leng;Sijia Feng;Xiaojie Ma

  • Light-YOLOv4: An Edge-Device Oriented Target Detection Method for Remote Sensing Images

    Xiaojie Ma;Kefeng Ji;Boli Xiong;Linbin Zhang

  • A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images

    Boli Xiong;Jing M. Chen;Gangyao Kuang

  • Classification via Sparse Representation of Steerable Wavelet Frames on Grassmann Manifold: Application to Target Recognition in SAR Image

    Ganggang Dong;Gangyao Kuang;Na Wang;Wei Wang

Frequent Co-Authors

Paul Fieguth
Paul Fieguth University of Waterloo
Matti Pietikäinen
Matti Pietikäinen University of Oulu
Jonathan Li
Jonathan Li University of Waterloo
Yulan Guo
Yulan Guo Sun Yat-sen University
Jiayi Ma
Jiayi Ma Wuhan University
Hongbin Zha
Hongbin Zha Peking University
David A. Clausi
David A. Clausi University of Waterloo
Hongyan Zhang
Hongyan Zhang China University of Geosciences
Jocelyn Chanussot
Jocelyn Chanussot Grenoble Alpes University
Yifang Ban
Yifang Ban Royal Institute of Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens doors to a variety of related online degrees that can help shape your career in technology, engineering, or science. Choosing an online degree offers flexibility and can make balancing work, study, and other commitments easier.

If you’re looking to complete your studies quickly, consider the fastest computer science degree options available online. These accelerated programs help students graduate sooner and enter the workforce faster.

For those interested in cross-disciplinary careers, studying an environmental engineering degree online provides a pathway into green technology and sustainability. If budget is a concern, the cheapest online mechanical engineering degree programs deliver affordability without compromising on credibility or quality.

Alternatively, a strong foundation in science can be gained with an online bachelor's degree in physics. No matter your choice, these online education paths can unlock a wide range of technology-driven and future-focused career opportunities.

Best Scientists Citing Gangyao Kuang

Trending Scientists