H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 35 Citations 6,570 125 World Ranking 5972 National Ranking 571

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Pattern recognition

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Computer vision. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Remote sensing. He mostly deals with Discriminative model in his studies of Pattern recognition.

Lefei Zhang combines subjects such as Contextual image classification, Feature learning and Face with his study of Hyperspectral imaging. His studies examine the connections between Deep learning and genetics, as well as such issues in Big data, with regards to Classifier. His Pixel study incorporates themes from Matrix decomposition and Cluster analysis.

His most cited work include:

  • Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (849 citations)
  • On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification (325 citations)
  • Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction (240 citations)

What are the main themes of his work throughout his whole career to date?

Lefei Zhang mainly investigates Artificial intelligence, Pattern recognition, Hyperspectral imaging, Computer vision and Discriminative model. He studied Artificial intelligence and Machine learning that intersect with Classifier. His Pattern recognition study integrates concerns from other disciplines, such as Embedding and Cluster analysis.

His Hyperspectral imaging research incorporates themes from Pixel, Spatial analysis and Support vector machine. His work carried out in the field of Discriminative model brings together such families of science as Data mining, Outlier and Hyperspectral image classification. His work investigates the relationship between Feature extraction and topics such as Kernel that intersect with problems in Kernel and Algorithm.

He most often published in these fields:

  • Artificial intelligence (85.21%)
  • Pattern recognition (61.27%)
  • Hyperspectral imaging (40.14%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (85.21%)
  • Pattern recognition (61.27%)
  • Feature (17.61%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Pattern recognition, Feature, Image and Inpainting. The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence. His Pattern recognition research includes themes of Subspace topology and Deep learning.

His biological study spans a wide range of topics, including Pyramid, Pixel and Pyramid. His research in Feature intersects with topics in Matching, Active learning and Kernel. Lefei Zhang works mostly in the field of Hyperspectral imaging, limiting it down to topics relating to Principal component analysis and, in certain cases, Subpixel rendering.

Between 2019 and 2021, his most popular works were:

  • Unsupervised Domain Adaptive Re-Identification: Theory and Practice (89 citations)
  • Dimensionality Reduction With Enhanced Hybrid-Graph Discriminant Learning for Hyperspectral Image Classification (45 citations)
  • Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification (30 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature, Machine learning and Image. His Support vector machine, Feature extraction and Convolutional neural network study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Encoder and Context, bridging the gap between disciplines. His Feature extraction research includes elements of Hyperspectral imaging, Kernel, Kernel and Hyperspectral image classification.

He has researched Hyperspectral imaging in several fields, including Embedding, Discriminant, Principal component analysis and Dimensionality reduction. In most of his Pattern recognition studies, his work intersects topics such as Data set. His study in the fields of Re identification and Feature vector under the domain of Machine learning overlaps with other disciplines such as Domain, Code and Scheme.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Liangpei Zhang;Lefei Zhang;Bo Du.
IEEE Geoscience and Remote Sensing Magazine (2016)

834 Citations

On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification

Lefei Zhang;Liangpei Zhang;Dacheng Tao;Xin Huang.
IEEE Transactions on Geoscience and Remote Sensing (2012)

371 Citations

Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction

Liangpei Zhang;Lefei Zhang;Dacheng Tao;Xin Huang.
IEEE Transactions on Geoscience and Remote Sensing (2013)

256 Citations

Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding

Lefei Zhang;Qian Zhang;Liangpei Zhang;Dacheng Tao.
Pattern Recognition (2015)

248 Citations

Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Bo Du;Wei Xiong;Jia Wu;Lefei Zhang.
IEEE Transactions on Systems, Man, and Cybernetics (2017)

246 Citations

Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image

Fulin Luo;Bo Du;Liangpei Zhang;Lefei Zhang.
IEEE Transactions on Systems, Man, and Cybernetics (2019)

166 Citations

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

Lefei Zhang;Qian Zhang;Bo Du;Xin Huang.
IEEE Transactions on Systems, Man, and Cybernetics (2018)

166 Citations

Sparse Transfer Manifold Embedding for Hyperspectral Target Detection

Lefei Zhang;Liangpei Zhang;Dacheng Tao;Xin Huang.
IEEE Transactions on Geoscience and Remote Sensing (2014)

160 Citations

Hyperspectral Remote Sensing Image Subpixel Target Detection Based on Supervised Metric Learning

Lefei Zhang;Liangpei Zhang;Dacheng Tao;Xin Huang.
IEEE Transactions on Geoscience and Remote Sensing (2014)

137 Citations

Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

Xian Guo;Xin Huang;Lefei Zhang;Liangpei Zhang.
IEEE Transactions on Geoscience and Remote Sensing (2016)

131 Citations

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

Contact us

Best Scientists Citing Lefei Zhang

Liangpei Zhang

Liangpei Zhang

Wuhan University

Publications: 118

Bo Du

Bo Du

Wuhan University

Publications: 90

Qian Du

Qian Du

Mississippi State University

Publications: 72

Licheng Jiao

Licheng Jiao

Xidian University

Publications: 68

Jon Atli Benediktsson

Jon Atli Benediktsson

University of Iceland

Publications: 42

Shutao Li

Shutao Li

Hunan University

Publications: 40

Xiuping Jia

Xiuping Jia

UNSW Sydney

Publications: 39

Jocelyn Chanussot

Jocelyn Chanussot

Grenoble Alpes University

Publications: 39

Antonio Plaza

Antonio Plaza

University of Extremadura

Publications: 37

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 29

Xiangrong Zhang

Xiangrong Zhang

Xidian University

Publications: 28

Pedram Ghamisi

Pedram Ghamisi

Helmholtz-Zentrum Dresden-Rossendorf

Publications: 26

Leyuan Fang

Leyuan Fang

Hunan University

Publications: 26

Jun Li

Jun Li

Sun Yat-sen University

Publications: 26

Qi Wang

Qi Wang

Northwestern Polytechnical University

Publications: 25

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 25

Something went wrong. Please try again later.