What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Synthetic aperture radar and Feature extraction.
Xiangrong Zhang regularly ties together related areas like Algorithm in his Artificial intelligence studies.
His Pattern recognition research includes themes of Clonal selection algorithm and Cluster analysis.
His work on Image retrieval as part of general Computer vision study is frequently connected to Process, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
As part of one scientific family, Xiangrong Zhang deals mainly with the area of Synthetic aperture radar, narrowing it down to issues related to the Speckle pattern, and often Feature detection, Contextual image classification and Visual Word.
The various areas that Xiangrong Zhang examines in his Hyperspectral imaging study include Co-training, Spatial analysis, Noise and Hyperspectral image classification.
His most cited work include:
- Spectral Clustering Ensemble Applied to SAR Image Segmentation (183 citations)
- Quantum-Inspired Immune Clonal Algorithm for Global Optimization (108 citations)
- Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection (91 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Computer vision.
His study in Artificial intelligence concentrates on Synthetic aperture radar, Image segmentation, Pixel, Support vector machine and Contextual image classification.
His Synthetic aperture radar research includes elements of Principal component analysis and Speckle noise, Speckle pattern.
His Cluster analysis research extends to Pattern recognition, which is thematically connected.
His work in Hyperspectral imaging addresses subjects such as Spectral bands, which are connected to disciplines such as Convolution.
His biological study spans a wide range of topics, including Kernel, Remote sensing, Deep learning and Feature.
He most often published in these fields:
- Artificial intelligence (78.53%)
- Pattern recognition (62.71%)
- Hyperspectral imaging (28.81%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (78.53%)
- Pattern recognition (62.71%)
- Feature extraction (22.60%)
In recent papers he was focusing on the following fields of study:
Xiangrong Zhang spends much of his time researching Artificial intelligence, Pattern recognition, Feature extraction, Convolutional neural network and Feature.
As part of his studies on Artificial intelligence, Xiangrong Zhang often connects relevant areas like Spatial analysis.
His Pattern recognition research is multidisciplinary, relying on both Synthetic aperture radar, Pixel and Representation.
The study incorporates disciplines such as Discriminative model, Remote sensing and Dimensionality reduction in addition to Feature extraction.
His Convolutional neural network research incorporates elements of Unsupervised learning and Hyperspectral image classification.
Xiangrong Zhang interconnects Image resolution and Statistical classification in the investigation of issues within Feature.
Between 2019 and 2021, his most popular works were:
- Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification (11 citations)
- Semi-Supervised Hyperspectral Image Classification via Spatial-Regulated Self-Training (11 citations)
- Fully Convolutional Network-Based Ensemble Method for Road Extraction From Aerial Images (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Xiangrong Zhang focuses on Artificial intelligence, Pattern recognition, Convolutional neural network, Feature extraction and Hyperspectral image classification.
His Artificial intelligence study often links to related topics such as Machine learning.
When carried out as part of a general Pattern recognition research project, his work on Hyperspectral imaging and Feature vector is frequently linked to work in Sparse matrix and Anomaly, therefore connecting diverse disciplines of study.
His Feature extraction study combines topics in areas such as Feature and Remote sensing.
His Hyperspectral image classification research incorporates themes from Adversarial system, Self training and Generative grammar.
His work on Image segmentation as part of general Segmentation research is often related to Base, thus linking different fields of science.
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