World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
67
Citations
17685
World Ranking
2197
National Ranking
298

Overview

Wei Li is affiliated with the Beijing Institute of Technology in China. Their research spans multiple fields, primarily within engineering and computer science, with a strong focus on subfields such as computer vision and pattern recognition, media technology, artificial intelligence, aerospace engineering, and atmospheric science.

Wei Li's work extensively covers topics including remote-sensing image classification, advanced image fusion techniques, remote sensing and land use, advanced neural network applications, domain adaptation and few-shot learning, infrared target detection methodologies, and image and signal denoising methods.

The scientist has contributed to a significant number of publications in prominent venues, including:

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

Wei Li's recent papers demonstrate contributions to advanced remote sensing and hyperspectral image classification research. Selected works include:

  • Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey, 2021, IEEE Geoscience and Remote Sensing Magazine
  • Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification, 2022, IEEE Transactions on Neural Networks and Learning Systems
  • Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification, 2021, IEEE Transactions on Neural Networks and Learning Systems
  • Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification, 2021, IEEE Transactions on Geoscience and Remote Sensing
  • Single-Source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification, 2023, IEEE Transactions on Image Processing

Wei Li frequently collaborates with other researchers whose names appear in multiple co-authored works. Frequent co-authors include Ran Tao, Mengmeng Zhang, Qian Du, Xiang-Gen Xia, and Shou Feng.

Best Publications

  • Hyperspectral Image Classification Using Deep Pixel-Pair Features

    Wei Li;Guodong Wu;Fan Zhang;Qian Du

  • Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

    Wei Li;Chen Chen;Hongjun Su;Qian Du

  • Collaborative Representation for Hyperspectral Anomaly Detection

    Wei Li;Qian Du

  • Multisource Remote Sensing Data Classification Based on Convolutional Neural Network

    Xiaodong Xu;Wei Li;Qiong Ran;Qian Du

  • Diverse Region-Based CNN for Hyperspectral Image Classification

    Mengmeng Zhang;Wei Li;Qian Du

  • Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

    Wei Li;S. Prasad;J. E. Fowler;L. M. Bruce

  • DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation

    Ruirui Li;Wenjie Liu;Lei Yang;Shihao Sun

  • Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification.

    Unknown

  • 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

  • HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers

    Ji He;Lina Zhao;Hongwei Yang;Mengmeng Zhang

  • Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery

    Wei Li;Guodong Wu;Qian Du

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

    Xin Wu;Wei Li;Danfeng Hong;Ran Tao

  • Combined sparse and collaborative representation for hyperspectral target detection

    Wei Li;Qian Du;Bing Zhang

  • Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

    Zhaokui Li;Ming Liu;Yushi Chen;Yimin Xu

  • Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification.

    Yuxiang Zhang;Wei Li;Mengmeng Zhang;Ying Qu

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

    Mengmeng Zhang;Wei Li;Qian Du;Lianru Gao

  • Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

    Chen Chen;Wei Li;Hongjun Su;Kui Liu

  • Nearest Regularized Subspace for Hyperspectral Classification

    Wei Li;Eric W. Tramel;Saurabh Prasad;James E. Fowler

  • Hyperspectral Anomaly Detection by Fractional Fourier Entropy

    Ran Tao;Xudong Zhao;Wei Li;Heng-Chao Li

  • Low-Rank and Sparse Representation for Hyperspectral Image Processing: A Review

    Jiangtao Peng;Weiwei Sun;Heng-Chao Li;Wei Li

  • Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification

    Wei Li;Qian Du

  • Land-use scene classification using multi-scale completed local binary patterns

    Chen Chen;Baochang Zhang;Hongjun Su;Wei Li

  • Joint Within-Class Collaborative Representation for Hyperspectral Image Classification

    Wei Li;Qian Du

  • Scene classification using local and global features with collaborative representation fusion

    Jinyi Zou;Wei Li;Chen Chen;Qian Du

Frequent Co-Authors

Qian Du
Qian Du Mississippi State University
Ran Tao
Ran Tao Beijing Institute of Technology
James E. Fowler
James E. Fowler Mississippi State University
Saurabh Prasad
Saurabh Prasad University of Houston
Heng-Chao Li
Heng-Chao Li Southwest Jiaotong University
Lianru Gao
Lianru Gao Aerospace Information Research Institute
Danfeng Hong
Danfeng Hong Chinese Academy of Sciences
Gottfried Kirchengast
Gottfried Kirchengast University of Graz
Juha Hyyppä
Juha Hyyppä Finnish Geospatial Research Institute
Xiang-Gen Xia
Xiang-Gen Xia University of Delaware

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