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.
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.
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.
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.
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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)
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.
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)
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)
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)
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)
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)
FACTORS AFFECTING THE QUALITY OF DTM GENERATION IN FORESTED AREAS
Hannu Hyyppä;Juha Hyyppä;Harri Kaartinen;Sanna Kaasalainen.
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.
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)
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