Lyytikainen Saarenmaa, P.
* 2010: Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images
* 2010: Evaluation Of Time-of-flight Range Cameras For Close Range Metrology Applications, An
* 2010: Using Stationary And Mobile Laser Scanner To Detect Forest Defoliation
* 2013: Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR
* 2015: Automation Aspects for the Georeferencing of Photogrammetric Aerial Image Archives in Forested Scenes
* 2015: Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level
* 2020: Using Multitemporal Hyper- and Multispectral UAV Imaging for Detecting Bark Beetle Infestation on Norway Spruce
* 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
* 2023: Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images
Includes: Lyytikainen Saarenmaa, P. Lyytikainen-Saarenmaa, P. Lyytikäinen-Saarenmaa, P. (Maybe also Lyytikaeinen-Saarenmaa, P.)Lyytikäinen-Saarenmaa, P.[Päivi] (Maybe also Lyytikaeinen-Saarenmaa, P.)
10 for Lyytikainen Saarenmaa, P.
Lyytikainen, M.[Minna]
* 2015: Automation Aspects for the Georeferencing of Photogrammetric Aerial Image Archives in Forested Scenes
Includes: Lyytikainen, M.[Minna] Lyytikäinen, M.[Minna] (Maybe also Lyytikaeinen, M.)
Lyytinen, H.
* 2008: offline/real-time artifact rejection strategy to improve the classification of multi-channel evoked potentials, An