Juntakut, P.
* 2019: Spatiotemporal Change of Urban Agriculture Using Google Earth Imagery: A Case of Municipality of Nakhonratchasima City, Thailand
Juntti, M.
* 2012: Fast Converging Algorithm for Weighted Sum Rate Maximization in Multicell MISO Downlink
* 2014: Conic Quadratic Programming Approach to Physical Layer Multicasting for Large-Scale Antenna Arrays, A
* 2020: Compressive sensed video recovery via iterative thresholding with random transforms
Includes: Juntti, M. Juntti, M.[Markku]
Junttila, J.
* 2018: OBF Database: A Large Face Video Database for Remote Physiological Signal Measurement and Atrial Fibrillation Detection, The
Junttila, S.[Samuli]
* 2015: Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees: Experiences from Laboratory Test
* 2017: Measuring Leaf Water Content with Dual-Wavelength Intensity Data from Terrestrial Laser Scanners
* 2020: Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning
* 2021: Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region: Comparison with Data from MODIS
* 2021: Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
* 2022: Adding single tree features and correcting edge tree effects enhance the characterization of seedling stands with single-photon airborne laser scanning
* 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: Novel Method to Simultaneously Measure Leaf Gas Exchange and Water Content, A
* 2023: Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images
Includes: Junttila, S.[Samuli] Junttila, S.[Sofia] Junttila, S.
10 for Junttila, S.
Junttila, V.[Virpi]
* 2013: Adaptive algorithm for large scale DTM interpolation from lidar data for forestry applications in steep forested terrain
* 2014: Algorithm for Extracting Digital Terrain Models under Forest Canopy from Airborne LiDAR Data
* 2015: Linear Models for Airborne-Laser-Scanning-Based Operational Forest Inventory With Small Field Sample Size and Highly Correlated LiDAR Data
* 2015: Moving Voxel Method for Estimating Canopy Base Height from Airborne Laser Scanner Data
* 2015: Variance-preserving mosaicing of multiple satellite images for forest parameter estimation: Radiometric normalization
* 2017: Large tree diameter distribution modelling using sparse airborne laser scanning data in a subtropical forest in Nepal
* 2017: LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon
* 2018: Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia
* 2018: Distribution Statistics Preserving Post-Processing Method With Plot Level Uncertainty Analysis for Remotely Sensed Data-Based Forest Inventory Predictions
Includes: Junttila, V.[Virpi] Junttila, V.
9 for Junttila, V.
Juntu, J.[Jaber]
* 2010: Segmentation Based Noise Variance Estimation from Background MRI Data
Juntunen, M.A.K.
* 2020: Framework for Photon Counting Quantitative Material Decomposition