What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Javier Plaza mostly deals with Hyperspectral imaging, Artificial intelligence, Pattern recognition, Feature extraction and Pixel.
The Hyperspectral imaging study combines topics in areas such as Image resolution, Spatial analysis, Contextual image classification and Image processing.
His studies in Contextual image classification integrate themes in fields like Endmember and Subpixel rendering.
His studies link Computer vision with Artificial intelligence.
His work deals with themes such as Convolutional neural network and Support vector machine, which intersect with Feature extraction.
Javier Plaza has researched Pixel in several fields, including Image restoration and Data mining, Data pre-processing.
His most cited work include:
- A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data (538 citations)
- Spatial/spectral endmember extraction by multidimensional morphological operations (468 citations)
- Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations (312 citations)
What are the main themes of his work throughout his whole career to date?
Javier Plaza mainly investigates Hyperspectral imaging, Artificial intelligence, Pattern recognition, Pixel and Computer vision.
The Endmember research Javier Plaza does as part of his general Hyperspectral imaging study is frequently linked to other disciplines of science, such as Imaging spectrometer, therefore creating a link between diverse domains of science.
In general Imaging spectrometer, his work in Airborne visible/infrared imaging spectrometer is often linked to Remote sensing linking many areas of study.
His Pattern recognition study combines topics in areas such as Overfitting, Curse of dimensionality and Contextual image classification, Image, Hyperspectral image classification.
His research in Pixel intersects with topics in Image resolution, Remote sensing application, Spectral bands, Spectral signature and Principal component analysis.
His Spatial analysis research is multidisciplinary, incorporating elements of Multispectral image and Graphics.
He most often published in these fields:
- Hyperspectral imaging (77.07%)
- Artificial intelligence (59.87%)
- Pattern recognition (40.76%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (59.87%)
- Hyperspectral imaging (77.07%)
- Pattern recognition (40.76%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Hyperspectral imaging, Pattern recognition, Convolutional neural network and Feature extraction.
His work on Deep learning, Visualization and Kernel as part of general Artificial intelligence research is often related to Earth observation, thus linking different fields of science.
His Hyperspectral imaging study integrates concerns from other disciplines, such as Image, Spatial analysis and Discriminative model.
His research on Pattern recognition also deals with topics like
- Curse of dimensionality, which have a strong connection to Pixel,
- Noise reduction and Probabilistic latent semantic analysis most often made with reference to Data cube.
His work carried out in the field of Convolutional neural network brings together such families of science as Artificial neural network, Spectral bands and Computer engineering.
His studies in Feature extraction integrate themes in fields like Feature and Spectral signature.
Between 2018 and 2021, his most popular works were:
- Deep learning classifiers for hyperspectral imaging: A review (105 citations)
- Capsule Networks for Hyperspectral Image Classification (86 citations)
- Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification (86 citations)
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