What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Her primary areas of investigation include Artificial intelligence, Hyperspectral imaging, Pattern recognition, Contextual image classification and Image resolution.
Her Artificial intelligence course of study focuses on Computer vision and Matching.
The concepts of her Hyperspectral imaging study are interwoven with issues in Image processing, Subspace topology and Support vector machine.
Her Pattern recognition research integrates issues from Mathematical morphology and Spatial analysis.
Her Contextual image classification research includes themes of Full spectral imaging, Feature vector and Linear discriminant analysis.
Her Multispectral image research incorporates elements of Multiresolution analysis, Feature learning and Sensor fusion.
Her 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 her work throughout her whole career to date?
Jocelyn Chanussot mainly focuses on Artificial intelligence, Hyperspectral imaging, Pattern recognition, Computer vision and Remote sensing.
Artificial intelligence is represented through her Image resolution, Pixel, Multispectral image, Feature extraction and Contextual image classification research.
Her research integrates issues of Spectral resolution, Image fusion, Convolutional neural network and Superresolution in her study of Image resolution.
Her Multispectral image research is multidisciplinary, relying on both Subspace topology and Sensor fusion.
Her biological study spans a wide range of topics, including Image processing, Algorithm, Spectral signature and Spatial analysis.
Pattern recognition is closely attributed to Image in her research.
She most often published in these fields:
- Artificial intelligence (67.87%)
- Hyperspectral imaging (53.11%)
- Pattern recognition (48.52%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (67.87%)
- Hyperspectral imaging (53.11%)
- Pattern recognition (48.52%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Hyperspectral imaging, Pattern recognition, Image resolution and Multispectral image.
In Artificial intelligence, Jocelyn Chanussot works on issues like Graph, which are connected to Laplacian matrix.
Her Hyperspectral imaging study necessitates a more in-depth grasp of Remote sensing.
Her Remote sensing research focuses on subjects like Earth observation, which are linked to Transfer of learning.
Her Pattern recognition study combines topics from a wide range of disciplines, such as Matrix decomposition, Embedding, Feature and Graph.
She combines subjects such as Iterative reconstruction and Superresolution with her study of Image resolution.
Between 2019 and 2021, her most popular works were:
- Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (61 citations)
- Graph Convolutional Networks for Hyperspectral Image Classification (50 citations)
- More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification (49 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Jocelyn Chanussot mostly deals with Hyperspectral imaging, Artificial intelligence, Pattern recognition, Image resolution and Multispectral image.
Her Hyperspectral imaging research incorporates themes from Pixel, Algorithm, Spectral signature, Iterative reconstruction and Sensor fusion.
Her Artificial intelligence study frequently draws connections to other fields, such as Graph.
Her Pattern recognition study incorporates themes from Image processing and Feature.
Her Image resolution study integrates concerns from other disciplines, such as Regularization, Sparse matrix and Superresolution.
In her study, Earth observation and Transfer of learning is inextricably linked to Remote sensing, which falls within the broad field of Deep learning.
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