What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Remote sensing
His primary areas of investigation include Remote sensing, Remote sensing, Cartography, Elevation and Aerial photography.
His work in Remote sensing addresses subjects such as Pixel, which are connected to disciplines such as Impervious surface.
Borrowing concepts from South carolina, he weaves in ideas under Remote sensing.
John R. Jensen interconnects Thematic Mapper and Data mining in the investigation of issues within Cartography.
His Elevation study combines topics in areas such as Terrain, Hydrology, Land cover, Vegetation and Lidar.
His study on Aerial photography also encompasses disciplines like
- Photogrammetry together with Lidar remote sensing,
- Panchromatic film which intersects with area such as Urban planning, Multispectral Scanner and Scale.
His most cited work include:
- Introductory Digital Image Processing: A Remote Sensing Perspective (3953 citations)
- Remote Sensing of the Environment: An Earth Resource Perspective (1620 citations)
- Introductory Digital Image Processing (712 citations)
What are the main themes of his work throughout his whole career to date?
John R. Jensen mainly focuses on Remote sensing, Remote sensing, Hydrology, Aerial photography and Thematic Mapper.
The various areas that John R. Jensen examines in his Remote sensing study include Land cover and Vegetation.
John R. Jensen has included themes like Change detection, Multispectral image, Geospatial analysis, Land use and Hazardous waste sites in his Remote sensing study.
His Geospatial analysis study typically links adjacent topics like Data science.
His study looks at the relationship between Hydrology and topics such as Transect, which overlap with Wetland classification.
His Geographic information system research is multidisciplinary, relying on both Resource management, Digital image processing, Water level, Photogrammetry and Digital elevation model.
He most often published in these fields:
- Remote sensing (62.93%)
- Remote sensing (31.03%)
- Hydrology (19.83%)
What were the highlights of his more recent work (between 2005-2019)?
- Remote sensing (62.93%)
- Remote sensing (31.03%)
- Hyperspectral imaging (7.76%)
In recent papers he was focusing on the following fields of study:
John R. Jensen spends much of his time researching Remote sensing, Remote sensing, Hyperspectral imaging, Artificial intelligence and Change detection.
His Remote sensing study frequently draws connections between adjacent fields such as Hydrology.
His studies in Remote sensing integrate themes in fields like Panchromatic film, Thematic map and Hazardous waste sites.
His Hyperspectral imaging research is multidisciplinary, incorporating perspectives in Irrigation, Biomass, Leaf area index, Nutrient and Multilayer perceptron.
His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with issues in Decision tree.
He has researched Image segmentation in several fields, including Pixel and Remote sensing application.
Between 2005 and 2019, his most popular works were:
- Remote Sensing of Urban/Suburban Infrastructure and Socio‐Economic Attributes (415 citations)
- Object-based change detection using correlation image analysis and image segmentation (315 citations)
- Fuzzy learning vector quantization for hyperspectral coastal vegetation classification (105 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Remote sensing
His primary scientific interests are in Remote sensing, Artificial intelligence, Change detection, Land cover and Remote sensing.
The concepts of his Remote sensing study are interwoven with issues in Hydrology and Cartography, Terrain.
His Change detection research incorporates themes from Calibration and Pattern recognition.
His work carried out in the field of Land cover brings together such families of science as Image enhancement, Satellite imagery and Hazardous waste sites.
Many of his research projects under Remote sensing are closely connected to Temporal resolution with Temporal resolution, tying the diverse disciplines of science together.
His Image segmentation research integrates issues from Pixel and Remote sensing application.
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