What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Image fusion and Pixel.
His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Hyperspectral image classification.
His Computer vision research incorporates elements of Classifier and Focus.
Support vector machine, Image segmentation and Contourlet are the core of his Pattern recognition study.
In his study, Digital image processing is strongly linked to Feature detection, which falls under the umbrella field of Image fusion.
His study looks at the relationship between Pixel and topics such as Principal component analysis, which overlap with Thematic Mapper.
His most cited work include:
- ADASYN: Adaptive synthetic sampling approach for imbalanced learning (1265 citations)
- Image Fusion With Guided Filtering (732 citations)
- Pixel-level image fusion (478 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Hyperspectral imaging, Computer vision and Feature extraction.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Pixel, Support vector machine, Sparse approximation, Image fusion and Image.
His Image fusion research is multidisciplinary, relying on both Digital image processing, Panchromatic film, Contourlet, Focus and Sensor fusion.
His Pattern recognition research is multidisciplinary, incorporating elements of Image resolution and Multispectral image.
Shutao Li studied Hyperspectral imaging and Anomaly detection that intersect with Detector.
Shutao Li works mostly in the field of Feature extraction, limiting it down to topics relating to Feature and, in certain cases, Object detection.
He most often published in these fields:
- Artificial intelligence (92.52%)
- Pattern recognition (77.21%)
- Hyperspectral imaging (39.80%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (92.52%)
- Pattern recognition (77.21%)
- Hyperspectral imaging (39.80%)
In recent papers he was focusing on the following fields of study:
Shutao Li mainly focuses on Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Convolutional neural network.
His Image resolution, Hyperspectral image classification, Iterative reconstruction, Deep learning and Image fusion investigations are all subjects of Artificial intelligence research.
His Image fusion research is under the purview of Computer vision.
His work carried out in the field of Pattern recognition brings together such families of science as Pixel, Image, Spatial analysis and Multispectral image.
His Hyperspectral imaging research also works with subjects such as
- Anomaly detection together with Detector,
- Matrix decomposition, which have a strong connection to Noise reduction and Algorithm.
As a part of the same scientific study, Shutao Li usually deals with the Feature extraction, concentrating on Feature and frequently concerns with Object detection.
Between 2018 and 2021, his most popular works were:
- Deep Learning for Hyperspectral Image Classification: An Overview (190 citations)
- Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification (64 citations)
- A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification (52 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Hyperspectral imaging, Artificial intelligence, Pattern recognition, Feature extraction and Image resolution.
His Hyperspectral imaging study combines topics from a wide range of disciplines, such as Matrix decomposition, Sparse matrix and Image processing.
Shutao Li combines subjects such as Dynamic range and Computer vision with his study of Artificial intelligence.
Pattern recognition and Image are commonly linked in his work.
His Feature extraction research includes themes of Smoothing, Convolutional neural network, Robustness and Hyperspectral image classification.
His studies in Image resolution integrate themes in fields like Regularization and Multispectral image.
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