What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Her primary areas of study are Artificial intelligence, Pattern recognition, Synthetic aperture radar, Contextual image classification and Change detection.
Her work deals with themes such as Machine learning, Markov process and Computer vision, which intersect with Artificial intelligence.
In her study, Parametric statistics, Estimation theory, Decision rule, Bayesian probability and Identification is strongly linked to Probability density function, which falls under the umbrella field of Pattern recognition.
The concepts of her Synthetic aperture radar study are interwoven with issues in Radar imaging, Parametric model and Expectation–maximization algorithm.
Her study in Contextual image classification is interdisciplinary in nature, drawing from both Markov random field and Multispectral image.
Her Change detection study also includes
- Thresholding and related Remote sensing,
- Adaptive optics which connect with Communication channel.
Her most cited work include:
- Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery (250 citations)
- Multimodal Classification of Remote Sensing Images: A Review and Future Directions (169 citations)
- Partially Supervised classification of remote sensing images through SVM-based probability density estimation (167 citations)
What are the main themes of her work throughout her whole career to date?
Artificial intelligence, Pattern recognition, Remote sensing, Change detection and Computer vision are her primary areas of study.
Many of her studies involve connections with topics such as Markov process and Artificial intelligence.
Her Pattern recognition research incorporates elements of Multispectral image, Markov chain and Expectation–maximization algorithm.
Gabriele Moser interconnects Earth observation, Image fusion and Sensor fusion in the investigation of issues within Remote sensing.
Her studies in Change detection integrate themes in fields like Transformation, Pixel, Deep learning and Thresholding.
Her Computer vision research focuses on Hyperspectral imaging and how it relates to Feature selection.
She most often published in these fields:
- Artificial intelligence (65.83%)
- Pattern recognition (47.74%)
- Remote sensing (31.16%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (65.83%)
- Pattern recognition (47.74%)
- Change detection (26.63%)
In recent papers she was focusing on the following fields of study:
Gabriele Moser mainly investigates Artificial intelligence, Pattern recognition, Change detection, Remote sensing and Pixel.
Artificial intelligence connects with themes related to Markov chain in her study.
Her Pattern recognition study combines topics in areas such as Markov process, Benchmark and Image translation.
Gabriele Moser has researched Change detection in several fields, including Synthetic aperture radar and Artificial neural network.
The study incorporates disciplines such as Cluster analysis, Earth observation and Radiometer in addition to Synthetic aperture radar.
Gabriele Moser usually deals with Pixel and limits it to topics linked to Random forest and Kernel regression, Data set and Hybrid system.
Between 2016 and 2021, her most popular works were:
- New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning (98 citations)
- Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest (63 citations)
- Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest (31 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Her primary areas of investigation include Artificial intelligence, Pattern recognition, Change detection, Sensor fusion and Random forest.
Her study of Deep learning is a part of Artificial intelligence.
Her Pattern recognition study integrates concerns from other disciplines, such as Image resolution, Mathematical morphology and Markov chain.
Her Image resolution study combines topics from a wide range of disciplines, such as Adaptive optics, Synthetic aperture radar, Sparse approximation, Random field and Quadtree.
Her work carried out in the field of Markov chain brings together such families of science as Contextual image classification, Focus and Feature extraction.
Her Random forest research integrates issues from Classifier, Pixel, Data-driven and Support vector machine.
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