World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
15105
World Ranking
3598
National Ranking
478

Overview

Lianru Gao is affiliated with the Aerospace Information Research Institute in China and has contributed extensively to research in engineering and computer science, particularly focusing on media technology and remote sensing applications. Their work encompasses a range of interdisciplinary topics related to image processing, remote sensing, and artificial intelligence.

Their research covers several main topics including:

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

Lianru Gao frequently publishes in prominent venues, with notable contributions to:

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

The following are some of their recent papers detailing their research focus and publication history:

  • "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)
  • "BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection," 2023, IEEE Transactions on Geoscience and Remote Sensing
  • "Deep learning in multimodal remote sensing data fusion: A comprehensive review," 2022, International Journal of Applied Earth Observation and Geoinformation

Collaborative efforts are significant in their research profile, with frequent coauthors including:

  • Bing Zhang
  • Xu Sun
  • Jocelyn Chanussot
  • Danfeng Hong
  • Lina Zhuang

Their contributions span 181 publications in engineering and 91 in computer science, with particular emphasis on media technology (159 publications) and computer vision and pattern recognition (68 publications). The subfield of atmospheric science also represents a substantial portion of their work, with 45 publications.

Best Publications

  • 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

  • Multisource Remote Sensing Data Classification Based on Convolutional Neural Network

    Xiaodong Xu;Wei Li;Qiong Ran;Qian Du

  • BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection

    Unknown

  • Progress and Challenges in Intelligent Remote Sensing Satellite Systems

    Unknown

  • Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery

    Qiandong Guo;Bing Zhang;Qiong Ran;Lianru Gao

  • Spectral Superresolution of Multispectral Imagery With Joint Sparse and Low-Rank Learning

    Lianru Gao;Danfeng Hong;Jing Yao;Bing Zhang

  • Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN

    Mengmeng Zhang;Wei Li;Qian Du;Lianru Gao

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

    Danfeng Hong;Lianru Gao;Renlong Hang;Bing Zhang

  • Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

    Danfeng Hong;Wei He;Naoto Yakoya;Jing Yao

  • Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion

    Unknown

  • Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery

    Bing Zhang;Shanshan Li;Xiuping Jia;Lianru Gao

  • Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations

    Lina Zhuang;Lianru Gao;Bing Zhang;Xiyou Fu

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

    Danfeng Hong;Lianru Gao;Jing Yao;Naoto Yokoya

  • NSCKL: Normalized Spectral Clustering With Kernel-Based Learning for Semisupervised Hyperspectral Image Classification

    Unknown

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

    Danfeng Hong;Wei He;Naoto Yokoya;Jing Yao

  • Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution

    Ke Zheng;Lianru Gao;Wenzhi Liao;Danfeng Hong

  • CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders

    Lianru Gao;Zhu Han;Danfeng Hong;Bing Zhang

  • Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution

    Unknown

  • Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Ant Colony Optimization (ACO) Algorithm

    Bing Zhang;Xun Sun;Lianru Gao;Lina Yang

  • Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks

    Xiaoyu Dong;Xu Sun;Xiuping Jia;Zhihong Xi

  • Subspace-Based Support Vector Machines for Hyperspectral Image Classification

    Lianru Gao;Jun Li;Mahdi Khodadadzadeh;Antonio J. Plaza

  • Building Extraction from High-Resolution Aerial Imagery Using a Generative Adversarial Network with Spatial and Channel Attention Mechanisms

    Xuran Pan;Xuran Pan;Fan Yang;Lianru Gao;Zhengchao Chen

  • A Comparative Study on Linear Regression-Based Noise Estimation for Hyperspectral Imagery

    Lianru Gao;Qian Du;Bing Zhang;Wei Yang

  • Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks

    Liwei Li;Zhi Yan;Qian Shen;Gang Cheng

  • Multiscale Residual Network With Mixed Depthwise Convolution for Hyperspectral Image Classification

    Hongmin Gao;Yao Yang;Chenming Li;Lianru Gao

Frequent Co-Authors

Bing Zhang
Bing Zhang Chinese Academy of Sciences
Jocelyn Chanussot
Jocelyn Chanussot Grenoble Alpes University
Wenzhi Liao
Wenzhi Liao Ghent University
Danfeng Hong
Danfeng Hong Chinese Academy of Sciences
Antonio Plaza
Antonio Plaza University of Extremadura
Qian Du
Qian Du Mississippi State University
Wei Li
Wei Li Beijing Institute of Technology
Paolo Gamba
Paolo Gamba University of Pavia
Xiuping Jia
Xiuping Jia University of New South Wales
Naoto Yokoya
Naoto Yokoya University of Tokyo

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