Saar, E.
* 2005: Analysis of the Spatial Distribution of Galaxies by Multiscale Methods
Saar, T.[Tonis]
* 2020: Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks
Saarakkala, S.[Simo]
* 2014: Local Binary Patterns to Evaluate Trabecular Bone Structure from Micro-CT Data: Application to Studies of Human Osteoarthritis
* 2017: Automatic Segmentation of Bone Tissue from Computed Tomography Using a Volumetric Local Binary Patterns Based Method
* 2017: Novel Method for Automatic Localization of Joint Area on Knee Plain Radiographs, A
* 2019: Automatic Regularization Method: An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data, An
* 2019: Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
* 2019: KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks
* 2020: Deep-learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography
* 2020: Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs
* 2024: Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data
Includes: Saarakkala, S.[Simo] Saarakkala, S.
9 for Saarakkala, S.
Saaranen, V.[Veikko]
* 2013: Comparison of Precise Leveling and Persistent Scatterer SAR Interferometry for Building Subsidence Rate Measurement, A
Saarela, P.[Petri]
* 2018: Virtual Structural Analysis of Jokisivu Open Pit Using Structure-from-Motion Unmanned Aerial Vehicles (UAV) Photogrammetry: Implications for Structurally-Controlled Gold Deposits in Southwest Finland
Saarela, S.[Svetlana]
* 2018: Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation
* 2018: Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data
* 2022: Importance of Calibration for Improving the Efficiency of Data Assimilation for Predicting Forest Characteristics
Saari, H.
* 2010: Evaluation Of A Lightweigth Uas-prototype For Hyperspectral Imaging.
* 2012: Hyperspectral Reflectance Signatures and Point Clouds for Precision Agriculture by light Weight UAV Imaging System
* 2012: Process for Radiometric Correction of UAV Image Blocks, A
* 2013: 2D Hyperspectral Frame Imager Camera Data in Photogrammetric Mosaicking
* 2013: miniature spectral imager for lightweight satellites, A
* 2013: New Light-Weight Stereosopic Spectrometric Airborne Imaging Technology for High-Resolution Environmental Remote Sensing: Case Studies in Water Quality Mapping
* 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
* 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: Individual Tree Detection and Classification 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
Includes: Saari, H. Saari, H.[Heikki]
13 for Saari, H.
Saari, P.
* 2016: Genre-Adaptive Semantic Computing and Audio-Based Modelling for Music Mood Annotation
Saari, R.K.[Rebecca K.]
* 2019: Quantifying PM2.5 mass concentration and particle radius using satellite data and an optical-mass conversion algorithm
Saari, T.[Timo]
* 2020: Geodetic SAR for Height System Unification and Sea Level Research: Observation Concept and Preliminary Results in the Baltic Sea
* 2022: Geodetic SAR for Height System Unification and Sea Level Research: Results in the Baltic Sea Test Network
Saarikoski, H.M.T.[Harri M. T.]
* 2006: Building an Optimal WSD Ensemble Using Per-Word Selection of Best System
Saarinen, J.
* 2001: Feature Selection Method Using Neural Network
* 2001: Optimized Singular Point Detection Algorithm for Fingerprint Images
Saarinen, J.P.
* 2015: Lossy-to-lossless progressive coding of depth-map images using competing constant and planar models
* 2015: Parametrizations of planar models for region-merging based lossy depth-map compression
Saarinen, K.
* 1994: Color image segmentation by a watershed algorithm and region adjacency graph processing
Saarinen, K.P.
* 1996: Digital Adaptive Robust Algorithms for Radar Image Filtering
Saarinen, N.[Ninni]
* 2013: Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning
* 2014: Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries
* 2015: Mapping the Risk of Forest Wind Damage Using Airborne Scanning LiDAR
* 2017: Feasibility of Terrestrial laser scanning for collecting stem volume information from single trees
* 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
* 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: Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands
* 2022: Adding single tree features and correcting edge tree effects enhance the characterization of seedling stands with single-photon airborne laser scanning
* 2022: Feasibility of Bi-Temporal Airborne Laser Scanning Data in Detecting Species-Specific Individual Tree Crown Growth of Boreal Forests
* 2022: Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season
* 2022: Terrestrial Laser Scanning in Assessing the Effect of Different Thinning Treatments on the Competition of Scots Pine (Pinus sylvestris L.) Forests
Includes: Saarinen, N.[Ninni] Saarinen, N.
16 for Saarinen, N.
Saartenoja, A.[Ari]
* 2019: Drone-Borne Hyperspectral and Magnetic Data Integration: Otanmäki Fe-Ti-V Deposit in Finland
* 2020: Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems