D-Index & Metrics Best Publications
Research.com 2022 Rising Star of Science Award Badge

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 47 Citations 9,405 144 World Ranking 3284 National Ranking 148
Rising Stars D-index 48 Citations 9,665 193 World Ranking 310 National Ranking 11

Research.com Recognitions

Awards & Achievements

2022 - Research.com Rising Star of Science Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Pattern recognition

Pedram Ghamisi focuses on Artificial intelligence, Hyperspectral imaging, Pattern recognition, Feature extraction and Convolutional neural network. Image processing, Object detection and Image resolution are the core of his Artificial intelligence study. His work deals with themes such as Spatial analysis, Pixel, Contextual image classification, Deep learning and Random forest, which intersect with Hyperspectral imaging.

His work in the fields of Support vector machine, Segmentation and Image segmentation overlaps with other areas such as Set. His Feature extraction research is multidisciplinary, incorporating perspectives in Data mining, Machine learning, Curse of dimensionality, Classifier and Hyperspectral image classification. His research investigates the connection with Convolutional neural network and areas like Overfitting which intersect with concerns in Principal component analysis, Convolution and Regularization.

His most cited work include:

  • Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (982 citations)
  • Cascaded Recurrent Neural Networks for Hyperspectral Image Classification (357 citations)
  • Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization (262 citations)

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

Pedram Ghamisi focuses on Artificial intelligence, Hyperspectral imaging, Pattern recognition, Feature extraction and Deep learning. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision. His Hyperspectral imaging study combines topics in areas such as Lidar, Random forest, Spatial analysis and Subspace topology.

The various areas that Pedram Ghamisi examines in his Pattern recognition study include Contextual image classification, Data mining and Data set. His research in Feature extraction intersects with topics in Feature, Curse of dimensionality, Hyperspectral image classification, Discriminative model and Kernel. His work in Deep learning covers topics such as Data science which are related to areas like Ensemble forecasting.

He most often published in these fields:

  • Artificial intelligence (77.89%)
  • Hyperspectral imaging (57.89%)
  • Pattern recognition (56.32%)

What were the highlights of his more recent work (between 2020-2021)?

  • Hyperspectral imaging (57.89%)
  • Artificial intelligence (77.89%)
  • Pattern recognition (56.32%)

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

His primary areas of study are Hyperspectral imaging, Artificial intelligence, Pattern recognition, Feature extraction and Convolutional neural network. He has researched Hyperspectral imaging in several fields, including Regularization, Wavelet, Feature and Overfitting. In the field of Artificial intelligence, his study on Deep learning, Iterative reconstruction and Noise overlaps with subjects such as Generalization and Dual graph.

His Pattern recognition study deals with Image intersecting with Spectral bands and Discriminative model. His Feature extraction research integrates issues from Image processing, Spatial analysis and Principal component analysis. His Convolutional neural network research includes themes of Subspace topology, Kernel and Sample.

Between 2020 and 2021, his most popular works were:

  • Hyperspectral Image Classification With Attention-Aided CNNs (8 citations)
  • Classification of Hyperspectral Images via Multitask Generative Adversarial Networks (7 citations)
  • Multiscale Densely-Connected Fusion Networks for Hyperspectral Images Classification (3 citations)

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 Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Yushi Chen;Hanlu Jiang;Chunyang Li;Xiuping Jia.
IEEE Transactions on Geoscience and Remote Sensing (2016)

1229 Citations

Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

Renlong Hang;Qingshan Liu;Danfeng Hong;Pedram Ghamisi.
IEEE Transactions on Geoscience and Remote Sensing (2017)

489 Citations

Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization

Pedram Ghamisi;Jon Atli Benediktsson.
IEEE Geoscience and Remote Sensing Letters (2015)

303 Citations

A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles

Pedram Ghamisi;Mauro Dalla Mura;Jon Atli Benediktsson.
IEEE Transactions on Geoscience and Remote Sensing (2015)

277 Citations

An efficient method for segmentation of images based on fractional calculus and natural selection

Pedram Ghamisi;Micael S. Couceiro;JóN Atli Benediktsson;Nuno M. F. Ferreira.
Expert Systems With Applications (2012)

267 Citations

Deep Learning for Hyperspectral Image Classification: An Overview

Shutao Li;Weiwei Song;Leyuan Fang;Yushi Chen.
IEEE Transactions on Geoscience and Remote Sensing (2019)

248 Citations

Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

Pedram Ghamisi;Micael S. Couceiro;Fernando M. L. Martins;Jon Atli Benediktsson.
IEEE Transactions on Geoscience and Remote Sensing (2014)

241 Citations

Advanced Spectral Classifiers for Hyperspectral Images: A review

Pedram Ghamisi;Javier Plaza;Yushi Chen;Jun Li.
IEEE Geoscience and Remote Sensing Magazine (2017)

233 Citations

Generative Adversarial Networks for Hyperspectral Image Classification

Lin Zhu;Yushi Chen;Pedram Ghamisi;Jon Atli Benediktsson.
IEEE Transactions on Geoscience and Remote Sensing (2018)

220 Citations

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Pedram Ghamisi;Naoto Yokoya;Jun Li;Wenzhi Liao.
IEEE Geoscience and Remote Sensing Magazine (2017)

173 Citations

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

Contact us

Best Scientists Citing Pedram Ghamisi

Qian Du

Qian Du

Mississippi State University

Publications: 59

Xiao Xiang Zhu

Xiao Xiang Zhu

German Aerospace Center

Publications: 58

Jocelyn Chanussot

Jocelyn Chanussot

Grenoble Alpes University

Publications: 54

Antonio Plaza

Antonio Plaza

University of Extremadura

Publications: 45

Liangpei Zhang

Liangpei Zhang

Wuhan University

Publications: 45

Licheng Jiao

Licheng Jiao

Xidian University

Publications: 40

Jon Atli Benediktsson

Jon Atli Benediktsson

University of Iceland

Publications: 34

Shutao Li

Shutao Li

Hunan University

Publications: 33

Wei Li

Wei Li

Chinese Academy of Sciences

Publications: 31

Xiuping Jia

Xiuping Jia

UNSW Sydney

Publications: 31

Hassan Ghassemian

Hassan Ghassemian

Tarbiat Modares University

Publications: 24

Jun Li

Jun Li

Sun Yat-sen University

Publications: 23

Jun Zhou

Jun Zhou

Griffith University

Publications: 21

Lorenzo Bruzzone

Lorenzo Bruzzone

University of Trento

Publications: 21

Javier Plaza

Javier Plaza

University of Extremadura

Publications: 20

Amir Mosavi

Amir Mosavi

Óbuda University

Publications: 20

Something went wrong. Please try again later.