What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Hyperspectral imaging, Artificial intelligence, Pattern recognition, Pixel and Remote sensing are his primary areas of study.
His Hyperspectral imaging research is multidisciplinary, incorporating elements of Contextual image classification, Feature extraction, Spatial analysis and Spectral signature.
Artificial intelligence connects with themes related to Computer vision in his study.
His studies deal with areas such as Subspace topology, Posterior probability and Curse of dimensionality as well as Pattern recognition.
His work on Endmember as part of general Pixel study is frequently connected to Set, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Antonio Plaza has researched Remote sensing in several fields, including Information extraction and Normalized Difference Vegetation Index.
His most cited work include:
- Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (1760 citations)
- Recent Advances in Techniques for Hyperspectral Image Processing (1191 citations)
- Hyperspectral Remote Sensing Data Analysis and Future Challenges (952 citations)
What are the main themes of his work throughout his whole career to date?
Antonio Plaza mainly focuses on Hyperspectral imaging, Artificial intelligence, Pattern recognition, Pixel and Computer vision.
His work on Endmember and Full spectral imaging as part of general Hyperspectral imaging research is frequently linked to Imaging spectrometer, bridging the gap between disciplines.
His Endmember research is multidisciplinary, relying on both Mixture model and Data set.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Spectral signature and Spatial analysis.
His Pattern recognition research integrates issues from Subspace topology and Image.
His work deals with themes such as Image resolution, Data mining and Remote sensing application, which intersect with Pixel.
He most often published in these fields:
- Hyperspectral imaging (75.12%)
- Artificial intelligence (61.56%)
- Pattern recognition (45.14%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (61.56%)
- Hyperspectral imaging (75.12%)
- Pattern recognition (45.14%)
In recent papers he was focusing on the following fields of study:
Antonio Plaza mostly deals with Artificial intelligence, Hyperspectral imaging, Pattern recognition, Convolutional neural network and Remote sensing.
His Artificial intelligence study is mostly concerned with Feature extraction, Deep learning, Image, Image resolution and Visualization.
His research on Hyperspectral imaging focuses in particular on Endmember.
Antonio Plaza interconnects Contextual image classification, Hyperspectral image classification, Pixel and Overfitting in the investigation of issues within Pattern recognition.
His studies in Convolutional neural network integrate themes in fields like Feature learning, Kernel, Discriminative model, Kernel and Image retrieval.
His Remote sensing research is multidisciplinary, incorporating perspectives in Pooling and Feature.
Between 2017 and 2021, his most popular works were:
- Image Segmentation Using Deep Learning: A Survey (107 citations)
- Deep learning classifiers for hyperspectral imaging: A review (105 citations)
- Capsule Networks for Hyperspectral Image Classification (86 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Hyperspectral imaging, Pattern recognition, Convolutional neural network and Feature extraction.
His biological study spans a wide range of topics, including Machine learning and Key.
His Hyperspectral imaging research is mostly focused on the topic Endmember.
The concepts of his Pattern recognition study are interwoven with issues in Topic model, Contextual image classification, Probabilistic logic, Spectral bands and Robustness.
His work carried out in the field of Feature extraction brings together such families of science as Feature, Covariance, Support vector machine, Visualization and Remote sensing.
His Remote sensing research includes elements of Image resolution and Pooling.
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