What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
His main research concerns Artificial intelligence, Hyperspectral imaging, Pattern recognition, Image processing and Computer vision.
His Artificial intelligence and Contextual image classification, Projection, Endmember, Gaussian noise and Independent component analysis investigations all form part of his Artificial intelligence research activities.
His Projection research incorporates themes from Projection pursuit and Least squares.
His research in Hyperspectral imaging intersects with topics in Pixel, Subpixel rendering, Multispectral pattern recognition, Algorithm and Multidimensional signal processing.
His Pattern recognition study incorporates themes from Subspace topology, Kullback–Leibler divergence and Signal processing.
His Computer vision research is multidisciplinary, relying on both Classifier and Anomaly detection.
His most cited work include:
- Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach (1187 citations)
- Hyperspectral Imaging: Techniques for Spectral Detection and Classification (846 citations)
- Estimation of number of spectrally distinct signal sources in hyperspectral imagery (754 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Hyperspectral imaging, Pattern recognition, Computer vision and Algorithm.
His Pixel, Image processing, Contextual image classification, Projection and Subpixel rendering investigations are all subjects of Artificial intelligence research.
He is involved in the study of Hyperspectral imaging that focuses on Endmember in particular.
Pattern recognition is closely attributed to Spectral signature in his research.
When carried out as part of a general Computer vision research project, his work on Image is frequently linked to work in Object detection, therefore connecting diverse disciplines of study.
His biological study spans a wide range of topics, including Mathematical optimization and Curse of dimensionality.
He most often published in these fields:
- Artificial intelligence (69.88%)
- Hyperspectral imaging (62.25%)
- Pattern recognition (52.61%)
What were the highlights of his more recent work (between 2015-2021)?
- Hyperspectral imaging (62.25%)
- Artificial intelligence (69.88%)
- Pattern recognition (52.61%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Hyperspectral imaging, Artificial intelligence, Pattern recognition, Algorithm and Anomaly detection.
His study in Hyperspectral imaging is interdisciplinary in nature, drawing from both Pixel, Image, Sparse matrix and Compressed sensing.
His study in the fields of Feature extraction under the domain of Artificial intelligence overlaps with other disciplines such as Object detection.
Chein-I Chang combines subjects such as Spatial analysis and Hyperspectral image classification with his study of Pattern recognition.
The various areas that he examines in his Algorithm study include Matrix decomposition, Simplex, Mathematical optimization, Dimensionality reduction and Endmember.
His Anomaly detection research is multidisciplinary, incorporating elements of Discriminative model, Anomaly, Constant false alarm rate and Detector.
Between 2015 and 2021, his most popular works were:
- A Review of Virtual Dimensionality for Hyperspectral Imagery (33 citations)
- Real-Time Progressive Hyperspectral Image Processing (29 citations)
- Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
Chein-I Chang focuses on Hyperspectral imaging, Artificial intelligence, Pattern recognition, Feature extraction and Minimum-variance unbiased estimator.
His work deals with themes such as Signal-to-noise ratio, Algorithm, Decorrelation and Real image, which intersect with Hyperspectral imaging.
His work in the fields of Artificial intelligence, such as Contextual image classification, overlaps with other areas such as A priori and a posteriori.
His Support vector machine, Classifier and Anomaly detection study in the realm of Pattern recognition interacts with subjects such as Object detection.
Chein-I Chang has included themes like Feature, Feature vector, Feature learning, Discriminative model and Robustness in his Feature extraction study.
His study focuses on the intersection of Minimum-variance unbiased estimator and fields such as Adaptive beamformer with connections in the field of Covariance matrix, Signal processing and Otsu's method.
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