H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 41 Citations 7,375 151 World Ranking 4353 National Ranking 406

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary scientific interests are in Artificial intelligence, Pattern recognition, Feature extraction, Hyperspectral imaging and Computer vision. All of his Artificial intelligence and Deep learning, Visualization, Image resolution, Data set and Feature investigations are sub-components of the entire Artificial intelligence study. His Pattern recognition research is multidisciplinary, incorporating perspectives in Pixel and Data mining.

In his study, Class, Benchmark, Range and Pyramid is strongly linked to Remote sensing, which falls under the umbrella field of Feature extraction. His Full spectral imaging study, which is part of a larger body of work in Hyperspectral imaging, is frequently linked to Non-negative matrix factorization and Graph, bridging the gap between disciplines. His Computer vision study integrates concerns from other disciplines, such as Data space and Manifold structure.

His most cited work include:

  • Remote Sensing Image Scene Classification: Benchmark and State of the Art (612 citations)
  • AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (519 citations)
  • Manifold Regularized Sparse NMF for Hyperspectral Unmixing (246 citations)

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

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Image. His study in Hyperspectral imaging, Discriminative model, Feature, Deep learning and Convolutional neural network is done as part of Artificial intelligence. In his research on the topic of Pattern recognition, Segmentation is strongly related with Pixel.

His research integrates issues of Visualization, Data mining, Representation, Machine learning and Remote sensing in his study of Feature extraction. Many of his research projects under Computer vision are closely connected to Set with Set, tying the diverse disciplines of science together. His biological study spans a wide range of topics, including Image resolution, Recurrent neural network, Class and Sentence.

He most often published in these fields:

  • Artificial intelligence (78.44%)
  • Pattern recognition (48.50%)
  • Feature extraction (25.75%)

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

  • Artificial intelligence (78.44%)
  • Pattern recognition (48.50%)
  • Feature extraction (25.75%)

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

Xiaoqiang Lu focuses on Artificial intelligence, Pattern recognition, Feature extraction, Convolutional neural network and Remote sensing. His study in Deep learning, Feature, Discriminative model, Subspace topology and Recurrent neural network falls within the category of Artificial intelligence. His study in Pattern recognition focuses on Hyperspectral imaging in particular.

His Feature extraction study incorporates themes from Object and Visualization. His Convolutional neural network study deals with Feature vector intersecting with Test set, Data set, Classifier and Training set. His Remote sensing research is multidisciplinary, relying on both Sentence, Image, Closed captioning and Aggregate.

Between 2019 and 2021, his most popular works were:

  • Remote Sensing Scene Classification by Gated Bidirectional Network (30 citations)
  • Spectral–Spatial Attention Network for Hyperspectral Image Classification (27 citations)
  • Multisource Compensation Network for Remote Sensing Cross-Domain Scene Classification (11 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

His main research concerns Artificial intelligence, Convolutional neural network, Feature extraction, Remote sensing and Pattern recognition. As a part of the same scientific family, Xiaoqiang Lu mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Cybernetics. Xiaoqiang Lu interconnects Artificial neural network and Discriminative model in the investigation of issues within Convolutional neural network.

His research in Feature extraction intersects with topics in Object and Feature learning. His Remote sensing study which covers Feature that intersects with Aggregate, Object detection and Representation. His work on Hyperspectral imaging as part of general Pattern recognition research is often related to Graph, thus linking different fields of science.

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

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Gong Cheng;Junwei Han;Xiaoqiang Lu.
Proceedings of the IEEE (2017)

799 Citations

AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Gui-Song Xia;Jingwen Hu;Fan Hu;Baoguang Shi.
IEEE Transactions on Geoscience and Remote Sensing (2017)

644 Citations

Manifold Regularized Sparse NMF for Hyperspectral Unmixing

Xiaoqiang Lu;Hao Wu;Yuan Yuan;Pingkun Yan.
IEEE Transactions on Geoscience and Remote Sensing (2013)

264 Citations

Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images

Xiaoqiang Lu;Yulong Wang;Yuan Yuan.
IEEE Transactions on Geoscience and Remote Sensing (2013)

248 Citations

Scene Recognition by Manifold Regularized Deep Learning Architecture

Yuan Yuan;Lichao Mou;Xiaoqiang Lu.
IEEE Transactions on Neural Networks (2015)

188 Citations

Remote Sensing Scene Classification by Unsupervised Representation Learning

Xiaoqiang Lu;Xiangtao Zheng;Yuan Yuan.
IEEE Transactions on Geoscience and Remote Sensing (2017)

177 Citations

Semi-Supervised Multitask Learning for Scene Recognition

Xiaoqiang Lu;Xuelong Li;Lichao Mou.
IEEE Transactions on Systems, Man, and Cybernetics (2015)

145 Citations

Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection

Yuan Yuan;Xiangtao Zheng;Xiaoqiang Lu.
IEEE Transactions on Image Processing (2017)

140 Citations

Exploring Models and Data for Remote Sensing Image Caption Generation

Xiaoqiang Lu;Binqiang Wang;Xiangtao Zheng;Xuelong Li.
IEEE Transactions on Geoscience and Remote Sensing (2018)

129 Citations

Hyperspectral Image Superresolution by Transfer Learning

Yuan Yuan;Xiangtao Zheng;Xiaoqiang Lu.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2017)

123 Citations

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Best Scientists Citing Xiaoqiang Lu

Liangpei Zhang

Liangpei Zhang

Wuhan University

Publications: 95

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

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Bo Du

Bo Du

Wuhan University

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Qi Wang

Qi Wang

Northwestern Polytechnical University

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Dacheng Tao

Dacheng Tao

University of Sydney

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Xiao Xiang Zhu

Xiao Xiang Zhu

German Aerospace Center

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Licheng Jiao

Licheng Jiao

Xidian University

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Xinbo Gao

Xinbo Gao

Chongqing University of Posts and Telecommunications

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Antonio Plaza

Antonio Plaza

University of Extremadura

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Qian Du

Qian Du

Mississippi State University

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Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

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Yuan Yuan

Yuan Yuan

Huawei Technologies (China)

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Chein-I Chang

Chein-I Chang

University of Maryland, Baltimore County

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Yanfei Zhong

Yanfei Zhong

Wuhan University

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Gui-Song Xia

Gui-Song Xia

Wuhan University

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Jun Li

Jun Li

Sun Yat-sen University

Publications: 24

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