单木分割；地面点滤波

单木分割与测量 (Individual Tree Segmentation & Measurement)

  1. Wilkes P (2022). TLS2trees: a scalable tree segmentation pipeline for TLS data. doi: 10.1101/2022.12.07.518693 github: philwilkes/TLS2trees
  2. Xu X; Calders K (2022). Topology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds. INT J APPL EARTH OBS. doi: 10.1016/j.jag.2022.103145 Data: . TLS Topology-based
  3. Xi Zhouxin (2022) ORCID. 3D Graph-Based Individual-Tree Isolation (Treeiso) from Terrestrial Laser Scanning Point Clouds. remote sensing. doi: 10.3390/rs14236116 Github: truebelief/artemis_treeiso LiDAR data: 10.20383/103.0624 TLS Graph-Based
  4. Harmon I (2022). Injecting Domain Knowledge Into Deep Neural Networks for Tree Crown Delineation. IEEE TRGS. doi: 10.1109/TGRS.2022.3216622
  5. Dong Y (2022). Unsupervised Semantic Segmenting TLS Data of Individual Tree Based on Smoothness Constraint Using Open-Source Datasets. IEEE TGRS. doi:10.1109/TGRS.2022.3218442 TreeSeg TLS
  6. Chang L (2022). A Two-Stage Approach for Individual Tree Segmentation From TLS Point Clouds. IEEE JSTARS. doi: 10.1109/JSTARS.2022.3212445 TreeSeg TLS
  7. Zhang Z (2022). Optimization Method of Airborne LiDAR Individual Tree Segmentation Based on Gaussian Mixture Model. remote sensing. doi: 10.3390/rs14236167
  8. Comesaña-Cebral (2021). Individual Tree Segmentation Method Based on Mobile Backpack LiDAR Point Clouds. sensors. doi:10.3390/s21186007 MLS TreeSeg
  9. Bienert (2021). Automatic extraction and measurement of individual trees from mobile laser scanning point clouds of forests. Annals of Botany. doi:10.1093/aob/mcab087 (Special Issue on 3D Forest Models and Laser Scanning Data) MLS TreeSeg
  10. Chen X (2021). Individual Tree Crown Segmentation Directly from UAV-Borne LiDAR Data Using the PointNet of Deep Learning. remote sensing. doi: 10.3390/f12020131 TreeSeg ULS Deep Learning
  11. Harikumar A (2020). A Crown Quantization-Based Approach to Tree-Species Classification Using High-Density Airborne Laser Scanning Data. IEEE TRGS. doi: 10.1109/TGRS.2020.3012343 ALS
  12. Chang L (2022). A Two-Stage Approach for Individual Tree Segmentation From TLS Point Clouds. IEEE JSTARS. doi: 10.1109/JSTARS.2022.3212445 TreeSeg TLS
  13. Corte A (2020). Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture. doi: 10.1016/j.compag.2020.105815 Measurement

叶木分离

  1. Tian Z (2022). Graph-Based Leaf–Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds. IEEE TGRS. doi: 10.1109/TGRS.2022.3218603
  2. Tan K (2021). Leaf and Wood Separation for Individual Trees Using the Intensity and Density Data of Terrestrial Laser Scanners. IEEE TGRS. doi: 10.1109/TGRS.2020.3032167
  3. Wan P (2021). A novel and efficient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level. MEE. doi: 10.1111/2041-210X.13715 Data: 10.1111/2041-210X.13715
  4. Hui Z (2021). Wood and leaf separation from terrestrial LiDAR point clouds based on mode points evolution. ISPRS Journal of Photogrammetry and Remote Sensing. doi: 10.1016/j.isprsjprs.2021.06.012

地面点滤波

  1. Li (2022). Terrain-Net: A Highly-Efficient, Parameter-Free, and Easy-to-Use Deep Neural Network for Ground Filtering of UAV LiDAR Data in Forested Environments. remote sensing. doi:10.3390/rs14225798. github: bjfu-lidar/Terrain-net ULS Filter

树种分类 (HSI/MSI)

  1. Chen L, Zhang X.L. (2022). Data Augmentation in Prototypical Networks for Forest Tree Species Classification Using Airborne Hyperspectral Images. IEEE TGRS. doi: 10.1109/TGRS.2022.3168054 HSI
  2. Chi D (2021). Urban Tree Species Classification Using Airborne Lidar and Hyperspectral Imagery. IEEE IGARSS. doi: 10.1109/IGARSS47720.2021.9554056 ALS HSI
  3. Lu X (2020). Tree Species Classification based on Airborne Lidar and Hyperspectral Data. IEEE IGARSS. doi: 10.1109/IGARSS39084.2020.9324266 ALS HSI
  4. Itakura K (2020). Tree Species Classification Using Leaf and Tree Trunk Images. IEEE IGARSS. doi: 10.1109/IGARSS39084.2020.9324126
  5. Li H (2020). CNN-Based Tree Species Classification Using Airborne Lidar Data and High-Resolution Satellite Image. IEEE IGARSS. doi: 10.1109/IGARSS39084.2020.9324011 ALS
  6. Liang J (2020). Forest Species Classification of UAV Hyperspectral Image Using Deep Learning. CAC. doi: 10.1109/CAC51589.2020.9327690 HSI

数据集

  1. Puliti S (2021). A New Drone Laser Scanning Benchmark Dataset for Characterization of Single-Tree and Forest Biophysical Properties. IGARSS. doi: 10.1109/IGARSS47720.2021.9553895 ULS
    适用于无人机的测量级激光扫描仪 (UAV-LS) 可以有效收集树木结构的精细三维 (3D) 信息，从而将森林的复杂性解析为离散的个体树木和物种以及不同的组成部分树的。目前的发展受到调查级 UAV-LS 数据的有限可用性以及缺乏用于开发和验证方法的公开基准数据集的阻碍。我们提出了一个新的基准数据集，由手动标记的 UAV-LS 数据组成，涵盖不同大陆和生态区域的森林。此类数据包含单树点云，每个点被分类为茎、枝和叶。该基准数据集为开发单树分割算法和验证现有算法提供了新的可能性。

update: 2022.12.06