Holobaca, I.H.[Iulian Horia]
* 2020: Potential of Night-Time Lights to Measure Regional Inequality
* 2020: VIIRS Nighttime Light Data for Income Estimation at Local Level
* 2022: Ratio of Land Consumption Rate to Population Growth Rate in the Major Metropolitan Areas of Romania
Includes: Holobaca, I.H.[Iulian Horia] Holobāca, I.H.[Iulian-Horia]
Holobar, A.
* 2015: Human-Machine Interfacing by Decoding the Surface Electromyogram
* 2016: Characterization of Human Motor Units From Surface EMG Decomposition
Holodovsky, V.[Vadim]
* 2020: Monotonicity Prior for Cloud Tomography
* 2021: 3DeepCT: Learning Volumetric Scattering Tomography of Clouds
Hologa, R.[Rafael]
* 2021: Societal Echo of Severe Weather Events: Ambient Geospatial Information (AGI) on a Storm Event, The
* 2021: Tree Species Classification in a Temperate Mixed Mountain Forest Landscape Using Random Forest and Multiple Datasets
Holohan, E.P.[Eoghan P.]
* 2019: PS-InSAR Analysis of Sentinel-1 Data for Detecting Ground Motion in Temperate Oceanic Climate Zones: A Case Study in the Republic of Ireland
Holohan, K.
* 2015: Benchmarking Platform for Mitotic Cell Classification of ANA IIF HEp-2 Images, A
Holopainen, M.
* 1998: Calibration of Digitized Aerial Photographs for Forest Stratification, The
* 2009: Combination of Low Pulse ALS Data and TERRASAR-X Radar images in the Estimation of Plot-Level Forest Variables
* 2009: Comparison of ALS-Based Low-Pulse Density Forest Inventories
* 2010: Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images
* 2010: Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in the Estimation of Plot-Level Forest Variables
* 2010: Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes
* 2011: Area-Based Snow Damage Classification of Forest Canopies Using Bi-Temporal Lidar Data
* 2011: Biomass Estimation of Individual Trees Using Stem and Crown Diameter TLS Measurements
* 2011: Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations
* 2011: Fusion of Individual Tree Detection and Visual Interpretation in Assessment of Forest Variables from Laser Point Clouds, The
* 2011: Predicting individual tree attributes from airborne laser point clouds based on the random forests technique
* 2011: Transmittance of Airborne Laser Scanning Pulses for Boreal Forest Elevation Modeling
* 2012: Advances in Forest Inventory Using Airborne Laser Scanning
* 2012: Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning
* 2012: Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data
* 2012: Detecting Changes in Forest Structure over Time with Bi-Temporal Terrestrial Laser Scanning Data
* 2012: Forest variable estimation using a high-resolution digital surface model
* 2012: International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning, An
* 2012: Laser Scanning in Forests
* 2013: Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning
* 2013: Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR
* 2013: Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data
* 2013: Individual Tree Biomass Estimation Using Terrestrial Laser Scanning
* 2013: Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR
* 2013: Single tree biomass modelling using airborne laser scanning
* 2014: Accuracy in Estimation of Timber Assortments and Stem Distribution: A Comparison of Airborne and Terrestrial Laser Scanning Techniques
* 2014: Automated Stem Curve Measurement Using Terrestrial Laser Scanning
* 2014: Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries
* 2014: Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery
* 2014: TerraSAR-X Stereo Radargrammetry and Airborne Scanning LiDAR Height Metrics in Imputation of Forest Aboveground Biomass and Stem Volume
* 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: Diameter distribution estimation with laser scanning based multisource single tree inventory
* 2015: Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees: Experiences from Laboratory Test
* 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: Terrestrial laser scanning in forest inventories
* 2016: Towards Automatic Single-sensor Mapping By Multispectral Airborne Laser Scanning
* 2017: Feasibility of Terrestrial laser scanning for collecting stem volume information from single trees
* 2017: Measuring Leaf Water Content with Dual-Wavelength Intensity Data from Terrestrial Laser Scanners
* 2017: Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning
* 2018: Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
* 2018: International benchmarking of terrestrial laser scanning approaches for forest inventories
* 2019: Detecting and characterizing downed dead wood using terrestrial laser scanning
* 2019: Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests
* 2019: Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data
* 2020: Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands
* 2020: Performance of terrestrial laser scanning to characterize managed Scots pine (Pinus sylvestris L.) stands is dependent on forest structural variation
* 2020: Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning
* 2020: Under-canopy UAV laser scanning for accurate forest field measurements
* 2020: Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands
* 2021: Hidden Cairns: A Case Study of Drone-Based ALS as an Archaeological Site Survey Method, The
* 2022: Adding single tree features and correcting edge tree effects enhance the characterization of seedling stands with single-photon airborne laser scanning
* 2022: Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season
* 2023: Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds
* 2023: New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings, A
Includes: Holopainen, M. Holopainen, M.[Markus]
56 for Holopainen, M.
Holota, R.[Radek]
* 2016: Fast image recognition based on n-tuple neural networks implemented in an FPGA
Holotyak, T.[Taras]
* 2014: Physical object identification using micro-structure images
* 2016: Local Active Content Fingerprint: Solutions for general linear feature maps
* 2016: Local active content fingerprinting: Optimal solution under linear modulation
* 2019: Defending Against Adversarial Attacks by Randomized Diversification
* 2019: Reconstruction of Privacy-Sensitive Data from Protected Templates
* 2019: Robustification of Deep Net Classifiers by Key Based Diversified Aggregation with Pre-Filtering
* 2022: Authentication Of Copy Detection Patterns Under Machine Learning Attacks: A Supervised Approach
Includes: Holotyak, T.[Taras] Holotyak, T.
7 for Holotyak, T.
Holowczak, R.D.
* 2002: experimental study on content-based image classification for satellite image databases, An
Holowka, E.M.[Eileen Mary]
* 2021: Principles for Designing an mHealth App for Participatory Research and Management of Chronic Pain