What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Hyperspectral imaging, Computer vision and Pixel.
His study in Artificial intelligence concentrates on Principal component analysis, Contextual image classification, Sparse approximation, Kernel and Extreme learning machine.
His work is connected to Feature extraction, Dimensionality reduction, Support vector machine, Discriminative model and Linear discriminant analysis, as a part of Pattern recognition.
His Hyperspectral imaging research is within the category of Remote sensing.
His work on RGB color model as part of general Computer vision research is frequently linked to Data transformation, bridging the gap between disciplines.
His Pixel study integrates concerns from other disciplines, such as Image processing, Anomaly detection, Representation and Detector.
His most cited work include:
- Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (1760 citations)
- Estimation of number of spectrally distinct signal sources in hyperspectral imagery (754 citations)
- A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification (380 citations)
What are the main themes of his work throughout his whole career to date?
Qian Du focuses on Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Pixel.
His study in Computer vision extends to Artificial intelligence with its themes.
The Pattern recognition study combines topics in areas such as Image and Representation.
His research integrates issues of Anomaly detection, Deep learning and Convolutional neural network in his study of Hyperspectral imaging.
His biological study spans a wide range of topics, including Feature, Artificial neural network, Cluster analysis, Local binary patterns and Kernel.
His Pixel research includes elements of Image resolution, Spectral signature and Mixture model.
He most often published in these fields:
- Artificial intelligence (82.97%)
- Pattern recognition (74.56%)
- Hyperspectral imaging (73.39%)
What were the highlights of his more recent work (between 2019-2021)?
- Hyperspectral imaging (73.39%)
- Artificial intelligence (82.97%)
- Pattern recognition (74.56%)
In recent papers he was focusing on the following fields of study:
Qian Du mostly deals with Hyperspectral imaging, Artificial intelligence, Pattern recognition, Feature extraction and Remote sensing.
Qian Du works on Hyperspectral imaging which deals in particular with Endmember.
His research ties Spatial analysis and Artificial intelligence together.
Qian Du combines subjects such as Pixel, Autoencoder and Residual with his study of Pattern recognition.
His Feature extraction study incorporates themes from Classifier, Object detection, Constant false alarm rate, Lidar and Joint.
Qian Du focuses mostly in the field of Remote sensing, narrowing it down to matters related to Contextual image classification and, in some cases, Identification.
Between 2019 and 2021, his most popular works were:
- More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification (49 citations)
- Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN (40 citations)
- Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Hyperspectral imaging, Artificial intelligence, Pattern recognition, Feature extraction and Convolutional neural network.
Remote sensing covers Qian Du research in Hyperspectral imaging.
His research in Artificial intelligence intersects with topics in Spectral signature and Non-negative matrix factorization.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Outlier and Hyperspectral image classification.
His Feature extraction research is multidisciplinary, incorporating elements of Very high resolution, Scale, Selection and Filter.
His work deals with themes such as Contextual image classification and Lidar, which intersect with Convolutional neural network.
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