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Description

Individual tree segmentation of LiDAR-derived point clouds using "random walker" algorithm. The algorithm is implemented using The Point Cloud Library (PCL) described in Shendryk, I., M. Broich, M. G. Tulbure and S. V. Alexandrov (2016). "Bottom-up delineation of individual trees from full-waveform airborne laser scans in a structurally complex eucalypt forest." Remote Sensing of Environment 173: 69-83.

and applied in:

Shendryk, I., M. Broich, M. G. Tulbure, A. McGrath, D. Keith and S. V. Alexandrov (2016). "Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest." Remote Sensing of Environment 187: 202-217.

Shendryk, I., M. Broich and M. G. Tulbure (2018). "Multi-sensor airborne and satellite data for upscaling tree number information in a structurally complex eucalypt forest." International Journal of Applied Earth Observation and Geoinformation 73: 397-406.

Acknowledgements

This work exists thanks to:

  1. Mirela Tulbure
  2. Mark Broich
  3. Sergey Alexandrov

Installation

  1. Install universal pre-requisites (Ubuntu 16.04):
 sudo apt-get update
 sudo apt-get install git build-essential linux-libc-dev
 sudo apt-get install cmake cmake-gui 
 sudo apt-get install libeigen3-dev
 sudo apt-get install libboost-all-dev
 sudo apt-get install libflann-dev
 sudo apt-get install libvtk6-qt-dev
 sudo apt-get install libqhull-dev
 sudo apt-get install libproj-dev
  1. Install PCL v1.8.1 (Ubuntu 16.04):
wget https://github.com/PointCloudLibrary/pcl/archive/pcl-1.8.1.tar.gz
tar -xf pcl-1.8.1.tar.gz
cd pcl-pcl-1.8.1 && mkdir build && cd build
cmake ..
make
sudo make install
  1. Clone this repository (recursively) and make out-of-source build:
git clone --recursive https://github.com/yurithefury/ForestMetrics.git ForestMetrics
cd ForestMetrics
mkdir build
cd build
cmake ..
make

Data

There are examples of LiDAR scenes in .pcd format in the 'data/' folder. Use las2pcd to convert .las to .pcd format.

Usage

Navigate to the 'data/' folder and run

cd ForestMetrics/data/
../bin/gui_delineation subset8.pcd

The program will load given file and present a pipeline for individual tree segmentation. To zoom in press r.

Visualizer interface

This is the standard PCL visualizer with several extensions. It has a list of objects available for visualization. To see it press h. The list will contain status indicators, short descriptions, and keys that are used to toggle display of the objects. For the random walker segmentation app it may look as follows:

                   Visualization objects
─────┬╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┬──────
  ☐  │ Display vertices                          │ V
  ☐  │ Display edges                             │ E
  ☐  │ Dispaly seeds                             │ S
  ☐  │ Dispaly points                            │ P
  ☐  │ Dispaly lines                             │ L
  ☒  │ Display trees                             │ T
─────┴╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┴──────

For example, press a to toggle graph adjacency edges display.

Pipeline:

The pipeline consists of 4 steps:

  1. Preprocessing
  2. Trunk (or tree top) detection
  3. Graph building
  4. Segmentation using "random walker" algorithm

Preprocessing

In this step apply a pass through filter to define points for further processing based on X and Y bounds. Basically, if your area of interest is too big you can cut out a small snippet for further evaluation.

Step_1

Trunk (or tree top) detection

In this step you can either define your "seeds" for random walker segmentation representing tree trunks (for bottom-up segmentation) or tree tops (for top-down segmentation). I suggest using bottom-up segmentation if the density of your LiDAR point clouds is >20 points/m2.

Seed selection works as follows:

  1. Bottom-up:

    1.1. Use another pass through filter to define points for tree trunk detection between Zmin and Zmax height.

    2.2. Use conditional euclidean clustering to segment individual tree trunks (i.e. seeds). You will have to adjust horizontal threshold, vertical threshold, cluster tolerance and minimum cluster size parameters.

    3.3. Use 3D line fitting to 'enrich' seeds along the tree trunk. You will have to adjust angular threshold and distance threshold parameters. When you are done with adjusting the parameters press enrich button to add points to existing trunks. Step_2

  2. Top-down:

    2.1. Use tree top detection using local maxima algorithm. You will have to adjust radius parameter.

When you are done with adjusting the parameters press use as seeds button.

Graph building

Here you can build your 3D graph for random walker segmentation. As part of this procedure you will have to define Graph builder and Edge weights parameters.

  1. Graph builder:

    1.1. Use Voxel grid to define the voxel resolution

    1.2. Use KNN to define k-nearest neighbors

    1.3. Use Radius to define the radius and maximum nearest neighbours

  2. Edge weights:

    Here you will have to adjust XYZ, Normal, Curvature, RGB and Verticality parameters, all of which are used for calculating the weights for graph edges.

When you are done with adjusting the parameters press Update. You can also press E button to display the edges of the graph. Step_3

Segmentation

After the seeds are defined and the graph is built press Segment button to perform random walker segmentation and visualize the results. Go to File → Save segmentation to write your segmentation results to a .pcd file. Step_4

Final notes

I'd love to see this pipeline implemented as a plugin for CloudCompare.