[]

Author: Pak Hui Ying
E-mail: pakhuiying95[at]gmail[dot]com

CoastalWQL

Source: Pak et al (2024) An Open-Source Tool for Drone-Based Mapping of Coastal Turbidity Using Push Broom Hyperspectral Imagery

CoastalWQL is an open-source software tailored for UAV-based water quality monitoring with a pushbroom hyperspectral imager. It performs the following workflow:

• Sensor: Bayspec's OCI-F Hyperspectral push broom sensor
• Interactive selection of regions for image stitching
• Interactive image alignment with real-time time delay correction
• Producing false-composite images from different hyperspectral bands (default is rgb)
• Sun glint correction using SUGAR to avoid over-correction in turbid/shallow regions
• Radiometric calibration and correction
• Image registration/direct-georeferencing
• de-striping of push broom hyperspectral imagery
• Image segmentation and masking (for masking out non-water objects)
• Extraction of spectral information based on supplied water quality information (e.g. coordinates, in-situ measurements)
• Prediction of water quality map using Nechad et al (2010)'s semi-analytical algorithm
• Extraction of weather variables (wind-speed, wind-direction, air-temperature, relative humidity) (only applicable for retrieving from Singapore's weather stations for now)

Dependencies

• python == 3.6
• conda
  • gdal == 3.0.2
  • py-xgboost == 0.90
  • rasterio == 1.1.0
• pip
  • numpy
  • pandas
  • matplotlib
  • sklearn
  • scipy
  • OpenCV
  • pysimplegui == 4.55.1

Usage

• Download anaconda (optional but recommended)
• Clone or download this repository into your preferred directory
• Create a virtual environment using conda env create -f env.yml (recommended) OR pip install -r CoastalWQL_requirements.txt
• In your preferred IDE, run GUI_platform.py
• For testing image segmentation and masking, users can supply their own model or try out using the supplied segmentation model xgb_segmentation.json
• For testing model prediction, users may supply their own model or try out the models in the turbidity_prediction folder
• Alternatively, run the code in the python notebook CoastalWQL_nb.ipynb
• Example inputs are provided but hyperspectral images and spectrometer not shared on github due to the sheer size of the data (~70-80GB of images).
• Example images and spectrometer data acquired from Nanyang Lake in Nanyang Technological University are shared here

For more details on CoastalWQL's features, do read Pak et al (2024): An Open-Source Tool for Drone-Based Mapping of Coastal Turbidity Using Push Broom Hyperspectral Imagery.

For a video demonstration, click here