D-Index & Metrics Best Publications

D-Index & Metrics 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.

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 43 Citations 6,759 235 World Ranking 5078 National Ranking 478

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Peijun Du mainly focuses on Artificial intelligence, Hyperspectral imaging, Pattern recognition, Contextual image classification and Feature extraction. The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Imaging spectrometer. His research in Hyperspectral imaging intersects with topics in Subspace topology, Data mining, Feature, Classifier and Pixel.

Pattern recognition is closely attributed to Random forest in his research. His Contextual image classification research incorporates themes from Computational complexity theory, Land cover, Machine learning, Remote sensing and AdaBoost. His Feature extraction study which covers Dimensionality reduction that intersects with Autoencoder, Data classification, Remote sensing and Probabilistic principal component analysis.

His most cited work include:

  • A review of supervised object-based land-cover image classification (315 citations)
  • Multiple Classifier System for Remote Sensing Image Classification: A Review (198 citations)
  • Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features (194 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Hyperspectral imaging, Remote sensing and Contextual image classification. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Computer vision. The various areas that he examines in his Pattern recognition study include Change detection and Random forest.

His Hyperspectral imaging research incorporates elements of Imaging spectrometer, Sparse approximation, Spatial analysis and Dimensionality reduction. His Remote sensing study incorporates themes from Land cover, Urbanization, Xuzhou and Vegetation. His Contextual image classification research includes elements of Data mining and Image fusion.

He most often published in these fields:

  • Artificial intelligence (58.68%)
  • Pattern recognition (53.72%)
  • Hyperspectral imaging (46.69%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (58.68%)
  • Pattern recognition (53.72%)
  • Hyperspectral imaging (46.69%)

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

His main research concerns Artificial intelligence, Pattern recognition, Hyperspectral imaging, Remote sensing and Feature extraction. His studies link Spatial analysis with Artificial intelligence. Peijun Du combines subjects such as Contextual image classification, Change detection and Feature with his study of Pattern recognition.

His Hyperspectral imaging study incorporates themes from Soil water, Soil test, Autoencoder and Hyperspectral image classification. His studies in Remote sensing integrate themes in fields like Random forest, Convolutional neural network and Calibration, Radiometric calibration. His Feature extraction research integrates issues from Subspace topology and Image segmentation.

Between 2018 and 2021, his most popular works were:

  • Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. (25 citations)
  • Advances of Four Machine Learning Methods for Spatial Data Handling: a Review (22 citations)
  • Advances of Four Machine Learning Methods for Spatial Data Handling: a Review (22 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Hyperspectral imaging, Pattern recognition, Feature extraction and Deep learning. Many of his studies on Artificial intelligence apply to Spatial analysis as well. The concepts of his Hyperspectral imaging study are interwoven with issues in Pixel, Soil water, Soil test and Environmental monitoring.

The Pattern recognition study which covers Hyperspectral image classification that intersects with Imaging spectrometer, Restricted Boltzmann machine, Boosting and Classifier. His biological study spans a wide range of topics, including Autoencoder, Markov random field, Region growing, Discriminative model and Hidden Markov model. His research in Discriminative model tackles topics such as Subspace topology which are related to areas like 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

A review of supervised object-based land-cover image classification

Lei Ma;Manchun Li;Xiaoxue Ma;Xiaoxue Ma;Liang Cheng.
Isprs Journal of Photogrammetry and Remote Sensing (2017)

616 Citations

Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features

Peijun Du;Alim Samat;Björn Waske;Sicong Liu.
Isprs Journal of Photogrammetry and Remote Sensing (2015)

348 Citations

Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

Jaime Zabalza;Jinchang Ren;Jiangbin Zheng;Huimin Zhao.
Neurocomputing (2016)

295 Citations

Multiple Classifier System for Remote Sensing Image Classification: A Review

Peijun Du;Junshi Xia;Wei Zhang;Kun Tan.
Sensors (2012)

281 Citations

${{ m E}^{2}}{ m LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification

Alim Samat;Peijun Du;Sicong Liu;Jun Li.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2014)

236 Citations

Integrating Multilayer Features of Convolutional Neural Networks for Remote Sensing Scene Classification

Erzhu Li;Junshi Xia;Peijun Du;Cong Lin.
IEEE Transactions on Geoscience and Remote Sensing (2017)

229 Citations

Hyperspectral Remote Sensing Image Classification Based on Rotation Forest

Junshi Xia;Peijun Du;Xiyan He;Jocelyn Chanussot.
IEEE Geoscience and Remote Sensing Letters (2014)

211 Citations

Information fusion techniques for change detection from multi-temporal remote sensing images

Peijun Du;Sicong Liu;Junshi Xia;Yindi Zhao.
Information Fusion (2013)

184 Citations

Fusion of Difference Images for Change Detection Over Urban Areas

Peijun Du;Sicong Liu;P. Gamba;Kun Tan.
urban remote sensing joint event (2011)

154 Citations

Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields

Junshi Xia;Jocelyn Chanussot;Peijun Du;Xiyan He.
IEEE Transactions on Geoscience and Remote Sensing (2015)

134 Citations

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