What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Hyperspectral imaging, Pattern recognition and Remote sensing.
His study in the field of Deep learning, Feature extraction, Pixel and Object detection is also linked to topics like Field.
Zhenwei Shi focuses mostly in the field of Pixel, narrowing it down to topics relating to Image and, in certain cases, Feature and Convolution.
Zhenwei Shi combines subjects such as Detector, Distortion, Haze, Convolutional neural network and Data set with his study of Computer vision.
The various areas that Zhenwei Shi examines in his Hyperspectral imaging study include Linear combination and Greedy algorithm.
His study on Remote sensing is often connected to Halo as part of broader study in Remote sensing.
His most cited work include:
- Object Detection in 20 Years: A Survey (135 citations)
- Ship Detection in Spaceborne Optical Image With SVD Networks (133 citations)
- Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature (123 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Hyperspectral imaging, Computer vision and Remote sensing.
His Artificial intelligence research focuses on Pixel, Image, Object detection, Feature extraction and Deep learning.
The concepts of his Pattern recognition study are interwoven with issues in Matched filter and Blind signal separation.
His Hyperspectral imaging study combines topics in areas such as Regularization, Optimization problem, Algorithm, Norm and Panchromatic film.
His Computer vision research is multidisciplinary, relying on both Haze, Convolutional neural network and Detector.
His research in the fields of Remote sensing overlaps with other disciplines such as Field.
He most often published in these fields:
- Artificial intelligence (69.49%)
- Pattern recognition (40.68%)
- Hyperspectral imaging (35.03%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (69.49%)
- Remote sensing (23.16%)
- Image (19.21%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Remote sensing, Image, Feature and Feature extraction.
His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Pattern recognition.
The Pattern recognition study combines topics in areas such as Pixel, Superresolution and Regularization.
His Remote sensing research incorporates themes from Object and Object detection.
His biological study spans a wide range of topics, including Representation, Algorithm and Discriminative model.
His research in Feature extraction tackles topics such as Artificial neural network which are related to areas like Linear programming, Image processing, Inverse problem and Source data.
Between 2019 and 2021, his most popular works were:
- A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection (20 citations)
- DSSNet: A Simple Dilated Semantic Segmentation Network for Hyperspectral Imagery Classification (13 citations)
- Domain Adaptation Based on Correlation Subspace Dynamic Distribution Alignment for Remote Sensing Image Scene Classification (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Remote sensing, Artificial intelligence, Feature extraction, Image and Hyperspectral imaging.
His Change detection study in the realm of Remote sensing connects with subjects such as Field.
His work on Contextual image classification and Deep learning as part of his general Artificial intelligence study is frequently connected to Reflection and Layer, thereby bridging the divide between different branches of science.
His Feature extraction study combines topics in areas such as Object, Subspace topology and Feature.
His Hyperspectral imaging study is concerned with Pattern recognition in general.
Many of his research projects under Pattern recognition are closely connected to Sparse matrix with Sparse matrix, tying the diverse disciplines of science together.
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