Honkamaa, P.[Petri]
* 2019: Auto-calibration of depth camera networks for people tracking
Honkasalo, J.
* 2007: Single-Tree Forest Inventory Using Lidar and Aerial Images for 3D Treetop Positioning, Species Recognition, Height and Crown Width Estimation
Honkavaara, E.[Eija]
* 2002: Investigations on System Calibration of GPS/IMU and Camera for Direct Georeferencing
* 2005: Factors affecting the quality of DTM generation in forested areas
* 2005: Using individual tree crown approach for forest volume extraction with aerial images and laser point clouds
* 2006: Geometric test field calibration of digital photogrammetric sensors
* 2007: Integration of Laser Scanning and Photogrammetry
* 2008: Permanent Test Field for Digital Photogrammetric Systems, A
* 2008: Radiometric Calibration and Characterization of Largeformat Digital Photogrammetric Sensors in a Test Field
* 2008: Registration of Airborne Laser Scanning Point Clouds with Aerial Images through Terrestrial Image Blocks
* 2009: Digital Airborne Photogrammetry: A New Tool for Quantitative Remote Sensing?: A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images
* 2010: In-situ digital airborne camera validation and certification: The future standard?
* 2010: Radiometric stability assessment of an airborne photogrammetric sensor in a test field
* 2011: EuroSDR project Radiometric aspects of digital photogrammetric images: Results of empirical phase
* 2011: SVM Classification of Tree Species Radiometric Signatures Based on the Leica ADS40 Sensor, An
* 2012: Assessment of Radiometric Correction Methods for ADS40 Imagery
* 2012: Hyperspectral Reflectance Signatures and Point Clouds for Precision Agriculture by light Weight UAV Imaging System
* 2012: Influence of solar elevation in radiometric and geometric performance of multispectral photogrammetry
* 2012: Process for Radiometric Correction of UAV Image Blocks, A
* 2013: Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging
* 2013: Automatic Storm Damage Detection in Forests Using High-Altitude Photogrammetric Imagery
* 2013: New Light-Weight Stereosopic Spectrometric Airborne Imaging Technology for High-Resolution Environmental Remote Sensing: Case Studies in Water Quality Mapping
* 2013: On the Use of RPAS in National Mapping: The EUROSDR Point of View
* 2013: Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables
* 2013: Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture
* 2013: Spectral Imaging from UAVS Under Varying Illumination Conditions
* 2014: Autonomous hyperspectral UAS photogrammetry for environmental monitoring applications
* 2014: Geometric processing workflow for vertical and oblique hyperspectral frame images collected using UAV
* 2014: Metrology of Directional, Spectral Reflectance Factor Measurements Based on Area Format Imaging by UAVs, The
* 2014: Metrology of Image Processing in Spectral Reflectance Measurement by UAV
* 2014: Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots, The
* 2014: WebGL Visualisation of 3D Environmental Models Based on Finnish Open Geospatial Data Sets
* 2015: Automation Aspects for the Georeferencing of Photogrammetric Aerial Image Archives in Forested Scenes
* 2015: Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes
* 2015: Forest Data Collection Using Terrestrial Image-Based Point Clouds From a Handheld Camera Compared to Terrestrial and Personal Laser Scanning
* 2015: Mapping the Risk of Forest Wind Damage Using Airborne Scanning LiDAR
* 2015: Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level
* 2016: Geometric And Reflectance Signature Characterization Of Complex Canopies Using Hyperspectral Stereoscopic Images From UAV And Terrestrial Platforms
* 2016: Orientation and Calibration Requirements for Hyperpectral Imaging Using UAVs: A Case Study
* 2016: Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spectral Ranges Onboard a Small Unmanned Airborne Vehicle (UAV)
* 2016: UAS Based Tree Species Identification Using The Novel FPI Based Hyperspectral Cameras In Visible, NIR and SWIR Spectral Ranges
* 2017: Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes
* 2017: Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods
* 2017: Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
* 2018: Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
* 2018: Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity
* 2018: Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features
* 2018: Novel Tilt Correction Technique for Irradiance Sensors and Spectrometers On-Board Unmanned Aerial Vehicles, A
* 2018: Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows
* 2018: Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment
* 2018: Successional stages and their evolution in tropical forests using multi-temporal photogrammetric surface models and superpixels
* 2019: Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System
* 2019: Assessment of Rgb and Hyperspectral UAV Remote Sensing for Grass Quantity and Quality Estimation
* 2019: Bundle Adjustment of a Time-Sequential Spectral Camera Using Polynomial Models
* 2019: Comparison of Pixel and Region-based Approaches for Tree Species Mapping in Atlantic Forest Using Hyperspectral Images Acquired By Uav
* 2019: Detecting Citrus Huanglongbing in Brazilian Orchards Using Hyperspectral Aerial Images
* 2019: Editorial for the Special Issue Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions
* 2019: Feasibility Study on Incremental Bundle Adjustment with Fisheye Images and Low-Cost Sensors, A
* 2019: Fisheye Image Matching Method Boosted by Recursive Search Space for Close Range Photogrammetry, A
* 2019: Generating a hyperspectral digital surface model using a hyperspectral 2D frame camera
* 2019: Performance Evaluation of Sequential Band Orientation By Polynomial Models in Hyperspectral Cubes Collected With Uav
* 2019: Preface - Isprs Workshop Hyperspectral Sensing Meets Machine Learning And Pattern Analysis (hypermlpa 2019)
* 2020: Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion
* 2020: Evaluation of Hyperspectral Multitemporal Information to Improve Tree Species Identification in the Highly Diverse Atlantic Forest
* 2020: Image-Based Real-Time Georeferencing Scheme for a UAV Based on a New Angular Parametrization, An
* 2020: Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands
* 2020: Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images, A
* 2020: Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
* 2020: Using Multitemporal Hyper- and Multispectral UAV Imaging for Detecting Bark Beetle Infestation on Norway Spruce
* 2021: Evaluation of SAR to Optical Image Translation Using Conditional Generative Adversarial Network for Cloud Removal In a Crop Dataset
* 2021: Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest
* 2021: Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases
* 2022: Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks
* 2022: Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network
* 2022: Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season
* 2022: Pix2pix Conditional Generative Adversarial Network with MLP Loss Function for Cloud Removal in a Cropland Time Series
* 2022: UAV in the advent of the twenties: Where we stand and what is next
* 2023: Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images
* 2023: Investigating Foliar Macro- and Micronutrient Variation with Chlorophyll Fluorescence and Reflectance Measurements at the Leaf and Canopy Scales in Potato
* 2023: New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings, A
* 2023: Novel Deep Multi-Image Object Detection Approach for Detecting Alien Barleys in Oat Fields Using RGB UAV Images, A
Includes: Honkavaara, E.[Eija] Honkavaara, E.
79 for Honkavaara, E.
Honkavaaraa, E.
* 2009: Status report of the EuroSDR project Radiometric aspects of digital photogrammetric airborne images
Honkela, A.
* 2011: Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model
Honko, P.[Piotr]
* 2015: Scalability of Data Decomposition Based Algorithms: Attribute Reduction Problem