reflectdetect 1.0.2

An automated workflow for in-flight radiometric calibration of UAV imagery

ReflectDetect

An innovative, fully automated command line software for in-flight radiometric calibration of UAV-mounted 2D snapshot multi-camera imagery

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Documentation

Download Example Datasets How to install?

Overview

Welcome to the official repository for the paper, "ReflectDetect: A software tool for AprilTag-guided in-flight radiometric calibration for UAV snapshot multi-cameras".

Abstract

Unmanned Aerial Vehicles (UAVs) equipped with optical sensors have transformed remote sensing in vegetation science by providing high-resolution, on-demand data, enhancing studies in forestry, agriculture, and environmental monitoring. However, accurate radiometric calibration of UAV imagery remains challenging. A common practice, using a single calibration target while holding the UAV-mounted camera close above it, is being criticized as the hemisphere is invisibly shaded and the reference images are not collected under flight conditions. ReflectDetect addresses these challenges by allowing in-flight radiometric calibration through automated detection via two different approaches: 1) a geotagging approach leveraging high-precision coordinates of the reflectance targets and 2) AprilTag-based detection, a visual fiducial system frequently used in robotics. ReflectDetect is available through a user-friendly command-line interface and open-source. It now enables users to design new in-flight calibration studies to eventually improve radiometric calibration in applied UAV remote sensing.

Workflow 1: Geolocation-Based Calibration

1. Know your calibration panel reflectance factors
   For each of your calibration panel, obtain the reflectance factor and ensure that it has a flat response according to the bands of your camera system. This information can be obtain from the manufacturer (for commercial panels) or by using a field spectrometer (for DIY panels). Save these values in a panel_properties.json file (see example data). In case you would like to build your own calibration panels, the following publication provides basic information on that topic:

   C. Wang and S. W. Myint, "A Simplified Empirical Line Method of Radiometric Calibration for Small Unmanned Aircraft Systems-Based Remote Sensing," in IEEE Journal of Selected Topics in Applied Earth > Observations and Remote Sensing, vol. 8, no. 5, pp. 1876-1885, May 2015, doi: 10.1109/JSTARS.2015.2422716.

2. Position the calibration panels in the field During placement, avoid that the panel(s) is/are shaded by adjacent plant or objects. Placing the panels in the center of your mapping area will increase the number of images that contain the panels.
3. Capture the locations of the panel corners
   We used an Ardusimple RTK Calibrated Surveyor Kit in our testing, save the data in a panel_locations.gpkg file (see example data).
4. Fly your drone mission, capturing images.
5. Rectifying and geo-reference the images
   We used Metashape in our testing
6. Run reflectdetect:
   Convert the orthophotos to reflectance data.

Workflow 2: AprilTag-Based Calibration

1. Gather Panel Reflectance Data
   For each of your calibration panel, obtain the reflectance factor and ensure that it has a flat response according to the bands of your camera system. This information can be obtain from the manufacturer (for commercial panels) or by using a field spectrometer (for DIY panels). Save these values in a panel_properties.json file (see example data).
2. Print an AprilTag for each panel, see Official AprilTag Repo
3. Field Setup
   Position the calibration panels and place the AprilTags according to the placement guide.
4. Fly your drone mission, capturing images.
5. Run reflectdetect:
   Convert the images to reflectance data.

Key Concepts and Vocabulary

• Panel: Calibration sheets placed in the field, used to compare captured intensity values in images with known reflectance values in the panel_properties.json file.

• AprilTag / Tag: A visual marker used for accurate detection in images.

• Field: The area captured by drone imagery.

• Image: Individual bands of captured data. reflectdetect assumes all images were taken at equal time intervals and named in the format *_{band_index}.tif.
  For example, IMG_0052_6.tif indicates the 6th band.

• Orthophoto / Photo: An image where all bands are combined and geo-referenced.

Docker Installation

Install Docker (for example using Docker Desktop)

Build the 3 different CLI tools using docker:

• docker build -t reflectdetect-apriltag-image -f Dockerfile_apriltag .
• docker build -t reflectdetect-converter-image -f Dockerfile_converter .
• docker build -t reflectdetect-geolocation-image -f Dockerfile_geolocation .

Run a CLI tool using docker after building:

• docker run -v /path/to/your/dataset:/data reflectdetect-apriltag-image
• docker run -v /path/to/your/dataset:/data reflectdetect-apriltag-image
• docker run -v /path/to/your/dataset:/data reflectdetect-apriltag-image

Manual Installation

Installing Python

To get started with this project, you'll need to have Python installed on your system. Follow the steps below to install Python:

1. Check if Python is Already Installed

Before installing, check if Python is already installed on your system:

• Windows/Mac/Linux:

  Open a terminal or command prompt and run the following command:

  python --version
  

  or

  python3 --version
  

  If Python is installed, you will see a version number. (Python 3.10 or higher is required)

2. Download and Install Python

If Python is not installed, follow these steps:

• Windows:

  1. Go to the official Python website.
  2. Download the latest version for Windows.
  3. Run the installer. Make sure to check the option "Add Python to PATH" during installation.
  4. Complete the installation process.

• Mac:

  1. Download the latest version of Python from the Python website.
  2. Open the downloaded .pkg file and follow the instructions to install.
  3. Alternatively, you can use Homebrew:

     brew install python
     

• Linux:

  1. Use the package manager for your distribution (e.g., apt for Ubuntu):

     sudo apt update
     sudo apt install python3
     

  2. For other distributions, refer to your package manager's documentation.

Installing ExifTool

ExifTool is an essential tool for working with image metadata in reflectdetect. Follow the instructions below to install ExifTool on your system.

Windows Installation

1. Download ExifTool:

   • Visit the ExifTool Home Page and download the latest 64bit Windows Executable (exiftool-xx.xx_64.zip).

2. Extract the Zip File:

   • Double-click the downloaded .zip file to open it, then drag the exiftool-xx.xx_64x folder to your Desktop ( where xx represents the version).

3. Prepare for Command Line Use:

   • Open the exiftool-12.96_xx folder on your Desktop.
   • Rename the exiftool(-k).exe file to exiftool.exe.
   • Move the folder folder to a convenient location (e.g., C:\ExifTool\ or another folder of your choice).

4. Add the Folder to the PATH:

   • Open the Start Menu, search and select Edit environment variables for your account.
   • Under the User variables section, find the variable named PATH and select it. Then click Edit.
   • In the new window, click New and enter the path to the folder where you moved exiftool.exe (e.g., C:\ExifTool).
   • Click OK to close all the windows.

5. Verify Installation:

   • Open Command Prompt (cmd) and type exiftool. You should see ExifTool's help information, confirming it is installed and recognized by your system.

macOS Installation

1. Download ExifTool:

   • Visit the ExifTool Home Page and download the latest MacOS Package (ExifTool-xx.xx.pkg).

2. Install ExifTool:

   • Double-click the downloaded .pkg file and follow the installation instructions.

3. Verify Installation:

   • Open Terminal and type exiftool. You should see ExifTool's help information, confirming it is installed and recognized by your system.

Linux Installation

1. Install via Package Manager:

   • On most Linux distributions, you can install ExifTool using the package manager. For example, on Ubuntu, run:

     sudo apt-get install libimage-exiftool-perl
     

2. Manual Install:

   • For manual installation instruction visit the ExifTool Install Page

3. Verify Installation:

   • Open your terminal and type exiftool. You should see ExifTool's help information, confirming it is installed and recognized by your system.

Installing reflectdetect

To install the reflectdetect CLI tools to your system, open a command line or terminal and run

pip install reflectdetect

Now the reflectdetect CLI Tools reflectdetect-converter, reflectdetect-apriltag and reflectdetect-geolocation should be available.

Usage

Find the information about running each workflow in their respective files:

• Workflow 1: Geolocation-Based Calibration
• Workflow 2: Apriltag-Based Calibration

Planned Features

• Support for unequal time intervals between images
• Customize parameters on a per panel basis
• Add dataset verification script
• Remove 5% of outliers in panel intensities value
• Allow running the calibration across multiple flight missions.
• Add absolute calibration functions and adjustments for more camera manufacturers.

Contributing

We welcome and encourage contributions! If you'd like to get involved, please feel free to:

• Fork the repository.
• Make your changes.
• Submit a pull request (PR).

We'll review your PR as soon as possible. Thank you for contributing!

Gallery

Apriltag Placement Guide

AI Usage Card

We used AI tools as follows: AI Usage Card

References

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