World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
13569
World Ranking
6053
National Ranking
801

Overview

Bing Zhang is affiliated with the Chinese Academy of Sciences in China. Their research primarily focuses on engineering and computer science, with significant contributions in media technology, computer vision and pattern recognition, atmospheric science, artificial intelligence, and aerospace engineering.

The scientist's main topics of work include:

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

Bing Zhang has published extensively, with frequent appearances in several academic venues. These include:

  • IEEE Transactions on Geoscience and Remote Sensing
  • arXiv (Cornell University)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • IEEE Geoscience and Remote Sensing Letters
  • Zenodo (CERN European Organization for Nuclear Research)

Recent papers by Bing Zhang feature research on hyperspectral image classification, multimodal remote sensing, and remote sensing foundation models. Notable 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
  • Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks, 2023, Remote Sensing of Environment

Collaborative efforts are evident in their work, with frequent co-authors including Lianru Gao, Danfeng Hong, Jocelyn Chanussot, Hongmin Gao, and Xu Sun. This network reflects interdisciplinary connections across remote sensing and pattern recognition fields.

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

  • A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information

    Miao Li;Shuying Zang;Bing Zhang;Shanshan Li

  • Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index

    Shenglei Wang;Shenglei Wang;Junsheng Li;Bing Zhang;Evangelos Spyrakos

  • 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

  • Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform

    Xiao Zhang;Liangyun Liu;Changshan Wu;Xidong Chen

  • Combined sparse and collaborative representation for hyperspectral target detection

    Wei Li;Qian Du;Bing Zhang

  • 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

  • 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

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

    Ke Zheng;Lianru Gao;Wenzhi Liao;Danfeng Hong

  • Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS

    Shenglei Wang;Shenglei Wang;Junsheng Li;Bing Zhang;Zhongping Lee

  • Application of hyperspectral remote sensing for environment monitoring in mining areas

    Bing Zhang;Di Wu;Li Zhang;Quanjun Jiao

  • Estimation and trend detection of water storage at Nam Co Lake, central Tibetan Plateau

    Bing Zhang;Yanhong Wu;Liping Zhu;Junbo Wang

  • 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

  • Estimating winter wheat plant water content using red edge parameters

    Liangyun Liu;Jihua Wang;Wenjiang Huang;Chunjiang Zhao

Frequent Co-Authors

Lianru Gao
Lianru Gao Aerospace Information Research Institute
Jocelyn Chanussot
Jocelyn Chanussot Grenoble Alpes University
Antonio Plaza
Antonio Plaza University of Extremadura
Wenzhi Liao
Wenzhi Liao Ghent University
Liangyun Liu
Liangyun Liu Chinese Academy of Sciences
Danfeng Hong
Danfeng Hong Chinese Academy of Sciences
Qian Du
Qian Du Mississippi State University
Xiuping Jia
Xiuping Jia University of New South Wales
Paolo Gamba
Paolo Gamba University of Pavia
Ming Ma
Ming Ma Chinese Academy of Sciences

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