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 58 Citations 11,105 261 World Ranking 1831 National Ranking 170

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of investigation include Artificial intelligence, Pattern recognition, Hyperspectral imaging, Pixel and Computer vision. His research ties Machine learning and Artificial intelligence together. His Pattern recognition study integrates concerns from other disciplines, such as Matrix decomposition and Feature.

His studies in Hyperspectral imaging integrate themes in fields like Object detection and Anomaly detection. His Pixel study combines topics from a wide range of disciplines, such as Change detection and Filter. His work is dedicated to discovering how Computer vision, Remote sensing are connected with Data cube and Tensor and other disciplines.

His most cited work include:

  • Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (849 citations)
  • Saliency-Guided Unsupervised Feature Learning for Scene Classification (332 citations)
  • Scene Classification via a Gradient Boosting Random Convolutional Network Framework (244 citations)

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

Bo Du mainly investigates Artificial intelligence, Pattern recognition, Hyperspectral imaging, Computer vision and Pixel. His Artificial intelligence course of study focuses on Machine learning and Representativeness heuristic. Bo Du works on Pattern recognition which deals in particular with Discriminative model.

His work focuses on many connections between Discriminative model and other disciplines, such as Dimensionality reduction, that overlap with his field of interest in Curse of dimensionality and Embedding. His study in the field of Endmember is also linked to topics like Detector. His Pixel study frequently draws parallels with other fields, such as Remote sensing.

He most often published in these fields:

  • Artificial intelligence (77.53%)
  • Pattern recognition (57.91%)
  • Hyperspectral imaging (37.97%)

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

  • Artificial intelligence (77.53%)
  • Pattern recognition (57.91%)
  • Convolutional neural network (10.44%)

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

Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Hyperspectral imaging are his primary areas of study. Artificial intelligence is often connected to Machine learning in his work. His research in Pattern recognition intersects with topics in Change detection and Data set.

He combines subjects such as Artificial neural network and Kernel with his study of Convolutional neural network. His research integrates issues of Pyramid, Image segmentation and Adaptation in his study of Deep learning. His Hyperspectral imaging research is multidisciplinary, relying on both Dimensionality reduction, Iterative reconstruction and Benchmark.

Between 2019 and 2021, his most popular works were:

  • Unsupervised Domain Adaptive Re-Identification: Theory and Practice (89 citations)
  • Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation (52 citations)
  • Dimensionality Reduction With Enhanced Hybrid-Graph Discriminant Learning for Hyperspectral Image Classification (45 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Bo Du spends much of his time researching Artificial intelligence, Pattern recognition, Kernel, Convolutional neural network and Machine learning. Artificial intelligence and Encoder are commonly linked in his work. Pattern recognition is represented through his Hyperspectral imaging and Feature extraction research.

His work carried out in the field of Hyperspectral imaging brings together such families of science as Sparse matrix, Anomaly detection, Mahalanobis distance and Graph. The various areas that he examines in his Feature extraction study include Attention network, Pixel and Robustness. His Convolutional neural network research incorporates themes from Discriminative model and Remote sensing.

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

Saliency-Guided Unsupervised Feature Learning for Scene Classification

Fan Zhang;Bo Du;Liangpei Zhang.
IEEE Transactions on Geoscience and Remote Sensing (2015)

392 Citations

Scene Classification via a Gradient Boosting Random Convolutional Network Framework

Fan Zhang;Bo Du;Liangpei Zhang.
IEEE Transactions on Geoscience and Remote Sensing (2016)

264 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

Random-Selection-Based Anomaly Detector for Hyperspectral Imagery

Bo Du;Liangpei Zhang.
IEEE Transactions on Geoscience and Remote Sensing (2011)

242 Citations

A Discriminative Metric Learning Based Anomaly Detection Method

Bo Du;Liangpei Zhang.
IEEE Transactions on Geoscience and Remote Sensing (2014)

236 Citations

A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection

Yuxiang Zhang;Bo Du;Liangpei Zhang;Shugen Wang.
IEEE Transactions on Geoscience and Remote Sensing (2016)

178 Citations

Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection

Fan Zhang;Bo Du;Liangpei Zhang;Miaozhong Xu.
IEEE Transactions on Geoscience and Remote Sensing (2016)

178 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

Best Scientists Citing Bo Du

Liangpei Zhang

Liangpei Zhang

Wuhan University

Publications: 98

Qian Du

Qian Du

Mississippi State University

Publications: 87

Antonio Plaza

Antonio Plaza

University of Extremadura

Publications: 55

Licheng Jiao

Licheng Jiao

Xidian University

Publications: 52

Xiaoqiang Lu

Xiaoqiang Lu

Chinese Academy of Sciences

Publications: 47

Yanfei Zhong

Yanfei Zhong

Wuhan University

Publications: 44

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 39

Xiao Xiang Zhu

Xiao Xiang Zhu

German Aerospace Center

Publications: 37

Shutao Li

Shutao Li

Hunan University

Publications: 35

Jun Li

Jun Li

Sun Yat-sen University

Publications: 34

Yuan Yuan

Yuan Yuan

Huawei Technologies (China)

Publications: 34

Wei Li

Wei Li

Chinese Academy of Sciences

Publications: 34

Xiuping Jia

Xiuping Jia

UNSW Sydney

Publications: 33

Jocelyn Chanussot

Jocelyn Chanussot

Grenoble Alpes University

Publications: 32

Gui-Song Xia

Gui-Song Xia

Wuhan University

Publications: 30

Qi Wang

Qi Wang

Northwestern Polytechnical University

Publications: 29

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.