ReHN 1.0.0

Height normalization for point clouds using the Re-Height Normalization (ReHN) algorithm.

ReHN: Point Cloud Re-Height Normalization

Introduction

This repository contains the python implementation of point cloud Re-Height Normalization (ReHN). The code is based on the paper:

Fu, B., Deng, L., Sun, W., He, H., Li, H., Wang, Y., Wang, Y., 2024. Quantifying vegetation species functional traits along hydrologic gradients in karst wetland based on 3D mapping with UAV hyperspectral point cloud. Remote Sens. Environ. 307, 114160. 10.1016/j.rse.2024.114160. https://www.sciencedirect.com/science/article/pii/S0034425724001718

What it contains?

• A python package rehn
• A command line tool rehn

You can simply use the command line tool for point cloud height normalization, or use the python package to integrate the height normalization into your own code.

Installation

Install from PyPI

pip install rehn

Install from source

Windows or Linux

git clone https://github.com/DLW3D/ReHN.git
cd ReHN
pip install -e . # -i https://pypi.mirrors.ustc.edu.cn/simple

Usage

Use as a command line tool

Make sure you have add the python bin path to the system environment variable PATH. you can find it by Windows:where.exe python, Linux:which python.

The python bin path may look like:

• Windows: C:\Users\username\AppData\Local\Programs\Python\Python39\Scripts
• Linux: /etc/miniconda3/envs/env_name/bin

Run the following command to normalize the point cloud:

rehn -i samples/HX_sample_with_ground.ply -o samples/outputs/HXs_ReHN.ply -n samples/outputs/HXs_ReHN.npy

Options

• -i or --pc_path: Required: Path to the input point cloud (PLY format)
• -o or --save_path: Required: Path to save the output point cloud (PLY format)
• -m or --dem_save_path: Path to save the DEM (npy format), default=None
• -mr or --dem_resolution: Resolution of the DEM, default=0.2 meters
• -f or --ground_feature_name: Name of the ground point feature in the point cloud, default=scalar_is_ground
• See more options by rehn -h

Use as a Python package

from rehn import height_norm_f
height_norm_f('samples/HX_sample_with_ground.ply', 
              'samples/outputs/HXs_ReHN.ply', 
              'samples/outputs/HXs_ReHN.npy',)

or

from rehn import height_norm, count_dem
xyz = ...  # Load your point cloud data
ground_mask = ...  # Load your basic ground mask
norm_z, ground_mask = height_norm(xyz, ground_mask)
count_dem(xyz, ground_mask, '/path/to/save/dem.npy')

Requirements

• pykdtree
• cloth-simulation-filter (CSF) (Optional: you need it if you don't have potential ground labels）
• numpy < 2 (if you don't need CSF, free to use numpy >= 2)

Citation

If you find this work useful, please consider citing the following paper:

@article{fu2024quantifying,
  title={Quantifying vegetation species functional traits along hydrologic gradients in karst wetland based on 3D mapping with UAV hyperspectral point cloud},
  author={Fu, Bojie and Deng, Liangji and Sun, Weixing and He, Honglin and Li, Hui and Wang, Yifan and Wang, Yifan},
  journal={Remote Sensing of Environment},
  volume={307},
  pages={114160},
  year={2024},
  publisher={Elsevier}
  doi={10.1016/j.rse.2024.114160}
}