What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Hyperspectral imaging and Remote sensing.
His study in Artificial intelligence concentrates on Feature extraction, Feature, Pixel, Image resolution and Contextual image classification.
His Pattern recognition study incorporates themes from Subspace topology and Multispectral image.
His research integrates issues of Regularization and Statistical classification in his study of Computer vision.
His research in Hyperspectral imaging intersects with topics in Spatial analysis, Detector, Imaging spectrometer, Anomaly detection and Noise reduction.
Liangpei Zhang has researched Remote sensing in several fields, including Cloud cover and Scale.
His most cited work include:
- Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources (893 citations)
- Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (849 citations)
- Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery (653 citations)
What are the main themes of his work throughout his whole career to date?
Liangpei Zhang mainly focuses on Artificial intelligence, Pattern recognition, Remote sensing, Hyperspectral imaging and Computer vision.
His work is connected to Pixel, Feature extraction, Image, Image resolution and Deep learning, as a part of Artificial intelligence.
Liangpei Zhang interconnects Discriminative model and Convolutional neural network in the investigation of issues within Feature extraction.
His Pattern recognition research focuses on Spatial analysis and how it relates to Regularization.
His work on Remote sensing as part of general Remote sensing study is frequently linked to Land cover, bridging the gap between disciplines.
In his research, Object detection is intimately related to Detector, which falls under the overarching field of Hyperspectral imaging.
He most often published in these fields:
- Artificial intelligence (68.60%)
- Pattern recognition (48.17%)
- Remote sensing (37.07%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (68.60%)
- Pattern recognition (48.17%)
- Hyperspectral imaging (36.82%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Hyperspectral imaging, Remote sensing and Deep learning.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Pixel, Convolutional neural network, Feature extraction, Image and Data set.
His Pattern recognition research includes themes of Spatial analysis, Feature and Hyperspectral image classification.
His study in Hyperspectral imaging is interdisciplinary in nature, drawing from both Subspace topology, Noise, Sparse matrix, Noise reduction and Image restoration.
In general Remote sensing, his work in Remote sensing is often linked to Coronavirus disease 2019 linking many areas of study.
His study in Deep learning is interdisciplinary in nature, drawing from both Field, Filter, Residual and Superresolution.
Between 2019 and 2021, his most popular works were:
- Deep learning in environmental remote sensing: Achievements and challenges (77 citations)
- Land-cover classification with high-resolution remote sensing images using transferable deep models (59 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
- Statistics
- Machine learning
Liangpei Zhang mainly investigates Artificial intelligence, Pattern recognition, Remote sensing, Hyperspectral imaging and Deep learning.
His Artificial intelligence research incorporates elements of Field and Scale.
His Pattern recognition research is multidisciplinary, incorporating elements of Spatial analysis, Line segment, Data set and Noise.
His Remote sensing research is multidisciplinary, relying on both Artificial neural network, Image, Convolutional neural network and Sensor fusion.
His Hyperspectral imaging research is multidisciplinary, incorporating perspectives in Graph, Embedding, Sparse matrix, Dimensionality reduction and Noise reduction.
His biological study spans a wide range of topics, including Shadow, Extreme value theory and Hyperspectral image classification.
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