What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Mihai Datcu focuses on Artificial intelligence, Feature extraction, Pattern recognition, Data mining and Synthetic aperture radar.
His study connects Computer vision and Artificial intelligence.
His study in Feature extraction is interdisciplinary in nature, drawing from both Training set, Geospatial analysis, Contextual image classification, Visual Word and Visualization.
The concepts of his Pattern recognition study are interwoven with issues in Regularization, Contrast and Representation.
His Data mining research includes elements of Data modeling, Earth observation, Information retrieval and Image retrieval.
His biological study spans a wide range of topics, including Image processing and Radar imaging.
His most cited work include:
- DOTA: A Large-Scale Dataset for Object Detection in Aerial Images (389 citations)
- Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks (305 citations)
- Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection (250 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Synthetic aperture radar and Data mining.
His work on Artificial intelligence deals in particular with Feature extraction, Image, Contextual image classification, Image resolution and Pixel.
His Feature extraction research includes themes of Feature and Search engine indexing.
His research is interdisciplinary, bridging the disciplines of Information extraction and Computer vision.
His research in Synthetic aperture radar intersects with topics in Inverse synthetic aperture radar, Radar imaging, Speckle pattern and Interferometry.
His studies in Data mining integrate themes in fields like Earth observation, Information retrieval and Image retrieval.
He most often published in these fields:
- Artificial intelligence (53.53%)
- Computer vision (29.97%)
- Pattern recognition (27.88%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (53.53%)
- Synthetic aperture radar (27.40%)
- Remote sensing (21.79%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Synthetic aperture radar, Remote sensing, Pattern recognition and Earth observation.
His work investigates the relationship between Artificial intelligence and topics such as Computer vision that intersect with problems in Electromagnetic spectrum.
He has included themes like Change detection, Scattering, Time–frequency analysis, Radar imaging and Interferometry in his Synthetic aperture radar study.
Mihai Datcu combines subjects such as Terrain, Bistatic radar, Range, Superresolution and Transmitter with his study of Remote sensing.
His Earth observation research incorporates elements of Context, Data mining, Land cover, Digital Earth and Data science.
Mihai Datcu works mostly in the field of Feature extraction, limiting it down to concerns involving Support vector machine and, occasionally, Visual Word.
Between 2017 and 2021, his most popular works were:
- DOTA: A Large-Scale Dataset for Object Detection in Aerial Images (389 citations)
- Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection (250 citations)
- Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Mihai Datcu mostly deals with Artificial intelligence, Synthetic aperture radar, Remote sensing, Pattern recognition and Deep learning.
His research combines Computer vision and Artificial intelligence.
His Synthetic aperture radar research is multidisciplinary, incorporating perspectives in Land cover, Visualization, Data mining and Earth observation.
His research integrates issues of Aperture, Superresolution and Bistatic radar in his study of Remote sensing.
His study focuses on the intersection of Pattern recognition and fields such as Data modeling with connections in the field of Data model.
His work deals with themes such as Multimedia, Discriminative model, Remote sensing image processing and Image retrieval, which intersect with Deep learning.
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