D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 36 Citations 6,670 159 World Ranking 5614 National Ranking 545

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Optics

Wei Li focuses on Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Kernel. Much of his study explores Artificial intelligence relationship to Computer vision. The Pattern recognition study combines topics in areas such as Local binary patterns and Linear combination.

His Hyperspectral imaging study introduces a deeper knowledge of Remote sensing. In his research on the topic of Support vector machine, Curse of dimensionality and Feature is strongly related with Principal component analysis. His work carried out in the field of Pixel brings together such families of science as Representation and Convolutional neural network.

His most cited work include:

  • Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification (355 citations)
  • Hyperspectral Image Classification Using Deep Pixel-Pair Features (343 citations)
  • Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis (322 citations)

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

His primary scientific interests are in Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Remote sensing. His research combines Computer vision and Artificial intelligence. His Hyperspectral imaging research focuses on Anomaly detection and how it relates to Detector.

He focuses mostly in the field of Feature extraction, narrowing it down to topics relating to Lidar and, in certain cases, Ranging. His work on Synthetic aperture radar as part of general Remote sensing research is often related to Occultation and Environmental science, thus linking different fields of science. His work in Support vector machine addresses subjects such as Kernel, which are connected to disciplines such as Extreme learning machine.

He most often published in these fields:

  • Artificial intelligence (67.92%)
  • Pattern recognition (61.77%)
  • Hyperspectral imaging (51.54%)

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

  • Artificial intelligence (67.92%)
  • Pattern recognition (61.77%)
  • Hyperspectral imaging (51.54%)

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

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Remote sensing. His study in Pattern recognition focuses on Sparse approximation in particular. His biological study spans a wide range of topics, including Anomaly detection, Support vector machine, Lidar, Discriminative model and Convolutional neural network.

His Feature extraction research is multidisciplinary, incorporating perspectives in Multispectral image, Artificial neural network, Clutter, Earth observation and Small target. Wei Li combines subjects such as Feature and Data set with his study of Remote sensing. His Deep learning research is multidisciplinary, relying on both Class and Classifier.

Between 2019 and 2021, his most popular works were:

  • Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN (40 citations)
  • Spatial–Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification (17 citations)
  • Remote sensing images super-resolution with deep convolution networks (10 citations)

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

  • Artificial intelligence
  • Machine learning
  • Optics

Wei Li spends much of his time researching Pattern recognition, Artificial intelligence, Hyperspectral imaging, Feature extraction and Discriminative model. His Pattern recognition study combines topics in areas such as Feature and Hyperspectral image classification. Artificial intelligence connects with themes related to Detector in his study.

The various areas that Wei Li examines in his Hyperspectral imaging study include Multispectral image, Support vector machine, Deep learning, Sparse approximation and Lidar. The concepts of his Feature extraction study are interwoven with issues in Pixel, Clutter, Approximation algorithm, Convolutional neural network and Synthetic data. His Pixel research includes elements of Contextual image classification and Transformer.

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

Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

Wei Li;Chen Chen;Hongjun Su;Qian Du.
IEEE Transactions on Geoscience and Remote Sensing (2015)

444 Citations

Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

Wei Li;S. Prasad;J. E. Fowler;L. M. Bruce.
IEEE Transactions on Geoscience and Remote Sensing (2012)

418 Citations

Hyperspectral Image Classification Using Deep Pixel-Pair Features

Wei Li;Guodong Wu;Fan Zhang;Qian Du.
IEEE Transactions on Geoscience and Remote Sensing (2017)

410 Citations

Collaborative Representation for Hyperspectral Anomaly Detection

Wei Li;Qian Du.
IEEE Transactions on Geoscience and Remote Sensing (2015)

299 Citations

Diverse Region-Based CNN for Hyperspectral Image Classification

Mengmeng Zhang;Wei Li;Qian Du.
IEEE Transactions on Image Processing (2018)

210 Citations

Nearest Regularized Subspace for Hyperspectral Classification

Wei Li;Eric W. Tramel;Saurabh Prasad;James E. Fowler.
IEEE Transactions on Geoscience and Remote Sensing (2014)

202 Citations

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

Chen Chen;Wei Li;Hongjun Su;Kui Liu.
Remote Sensing (2014)

200 Citations

Multisource Remote Sensing Data Classification Based on Convolutional Neural Network

Xiaodong Xu;Wei Li;Qiong Ran;Qian Du.
IEEE Transactions on Geoscience and Remote Sensing (2018)

170 Citations

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

Wei Li;Qian Du.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2014)

147 Citations

Joint Within-Class Collaborative Representation for Hyperspectral Image Classification

Wei Li;Qian Du.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2014)

145 Citations

Best Scientists Citing Wei Li

Qian Du

Qian Du

Mississippi State University

Publications: 85

Liangpei Zhang

Liangpei Zhang

Wuhan University

Publications: 58

Shutao Li

Shutao Li

Hunan University

Publications: 52

Jocelyn Chanussot

Jocelyn Chanussot

Grenoble Alpes University

Publications: 45

Jon Atli Benediktsson

Jon Atli Benediktsson

University of Iceland

Publications: 43

Licheng Jiao

Licheng Jiao

Xidian University

Publications: 42

Pedram Ghamisi

Pedram Ghamisi

Helmholtz-Zentrum Dresden-Rossendorf

Publications: 38

Xiuping Jia

Xiuping Jia

UNSW Sydney

Publications: 38

Saurabh Prasad

Saurabh Prasad

University of Houston

Publications: 35

Leyuan Fang

Leyuan Fang

Hunan University

Publications: 32

Bo Du

Bo Du

Wuhan University

Publications: 30

Antonio Plaza

Antonio Plaza

University of Extremadura

Publications: 29

Xiao Xiang Zhu

Xiao Xiang Zhu

German Aerospace Center

Publications: 25

Xiangrong Zhang

Xiangrong Zhang

Xidian University

Publications: 25

Jun Li

Jun Li

Sun Yat-sen University

Publications: 24

Lefei Zhang

Lefei Zhang

Wuhan University

Publications: 22

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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

Contact us
Something went wrong. Please try again later.