D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 4,403 110 World Ranking 7270 National Ranking 56

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Computer vision
  • Ecology

Her main research concerns Remote sensing, Point cloud, Photogrammetry, Artificial intelligence and Laser scanning. Her Remote sensing study frequently draws parallels with other fields, such as Taiga. Her Point cloud research is multidisciplinary, incorporating perspectives in Tree, Forest inventory, Real-time computing and Stereoscopy.

Her work deals with themes such as Remote sensing, Image sensor and Radiometry, which intersect with Photogrammetry. Artificial intelligence is closely attributed to Computer vision in her work. Eija Honkavaara interconnects Automated data processing and Sample in the investigation of issues within Laser scanning.

Her most cited work include:

  • Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture (293 citations)
  • Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera (193 citations)
  • Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging (171 citations)

What are the main themes of her work throughout her whole career to date?

Remote sensing, Hyperspectral imaging, Photogrammetry, Artificial intelligence and Point cloud are her primary areas of study. Her work carried out in the field of Remote sensing brings together such families of science as Tree, Forest inventory and Precision agriculture. Her research integrates issues of RGB color model, Pixel, Spectral bands and Interferometry in her study of Hyperspectral imaging.

She combines subjects such as Canopy, Tree canopy, Remote sensing application and Laser scanning with her study of Photogrammetry. Her Artificial intelligence research is multidisciplinary, relying on both Point, Machine learning, Computer vision and Pattern recognition. Her studies in Point cloud integrate themes in fields like Image sensor, Random forest, Geospatial analysis and Basal area.

She most often published in these fields:

  • Remote sensing (66.67%)
  • Hyperspectral imaging (42.86%)
  • Photogrammetry (38.69%)

What were the highlights of her more recent work (between 2018-2021)?

  • Hyperspectral imaging (42.86%)
  • Remote sensing (66.67%)
  • Artificial intelligence (29.76%)

In recent papers she was focusing on the following fields of study:

Eija Honkavaara mainly focuses on Hyperspectral imaging, Remote sensing, Artificial intelligence, Tree and Point cloud. Eija Honkavaara has included themes like Spectral bands, Convolutional neural network, Tree species and Random forest in her Hyperspectral imaging study. Her Remote sensing research includes themes of Pixel, Forest inventory, Precision agriculture and Canopy.

Eija Honkavaara has researched Artificial intelligence in several fields, including Machine learning and Computer vision. Her Tree study integrates concerns from other disciplines, such as Forest management, Thinning and Taiga. Eija Honkavaara regularly ties together related areas like Photogrammetry in her Point cloud studies.

Between 2018 and 2021, her most popular works were:

  • Pre-crop Values From Satellite Images for Various Previous and Subsequent Crop Combinations. (68 citations)
  • Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements (31 citations)
  • A Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images (17 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Optics

Eija Honkavaara mainly investigates Hyperspectral imaging, Remote sensing, Tree, Artificial intelligence and Forest inventory. Her studies deal with areas such as Organic matter, Dry matter, Multispectral image, Spectral bands and Tree species as well as Hyperspectral imaging. Her research in Remote sensing is mostly concerned with Photogrammetry.

Her Photogrammetry study combines topics from a wide range of disciplines, such as Epipolar geometry and Multi camera. Her biological study spans a wide range of topics, including Computer vision and Pattern recognition. Her study in Forest inventory is interdisciplinary in nature, drawing from both Lidar, Canopy, Tree canopy and Point cloud.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture

Eija Honkavaara;Heikki Saari;Jere Kaivosoja;Ilkka Pölönen.
Remote Sensing (2013)

438 Citations

Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

Tomi Rosnell;Eija Honkavaara.
Sensors (2012)

298 Citations

Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level

Roope Näsi;Eija Honkavaara;Päivi Marja Emilia Lyytikäinen-Saarenmaa;Minna Blomqvist.
Remote Sensing (2015)

246 Citations

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Olli Nevalainen;Eija Honkavaara;Sakari Tuominen;Niko Viljanen.
Remote Sensing (2017)

243 Citations

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

Helge Aasen;Eija Honkavaara;Arko Lucieer;Pablo J. Zarco-Tejada.
Remote Sensing (2018)

216 Citations

Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables

Kimmo Nurminen;Mika Karjalainen;Xiaowei Yu;Juha Hyyppä.
Isprs Journal of Photogrammetry and Remote Sensing (2013)

129 Citations

Digital Airborne Photogrammetry—A New Tool for Quantitative Remote Sensing?—A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images

Eija Honkavaara;Roman Arbiol;Lauri Markelin;Lucas Martinez.
Remote Sensing (2009)

123 Citations

FACTORS AFFECTING THE QUALITY OF DTM GENERATION IN FORESTED AREAS

Hannu Hyyppä;Juha Hyyppä;Harri Kaartinen;Sanna Kaasalainen.
(2005)

122 Citations

A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone

Niko Viljanen;Eija Honkavaara;Roope Näsi;Teemu Hakala.
Agriculture (2018)

104 Citations

Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods

Ville V. Lehtola;Harri Kaartinen;Andreas Nüchter;Risto Kaijaluoto.
Remote Sensing (2017)

104 Citations

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