Research output per year
Research output per year
Description
This dataset contains:
1) The MATLAB scripts "PlotFigures.m" and "metricsProper.m" to reproduce the different figures shown in the publication.
2) The datasets used for the creation of the figures are saved inside "modelledData.m", "SIOPS.xls", "InSitu_Stations_Coordinates.csv" and the "LandsatImage.TIFF".
3) The figures inside the "Figures" folder in .TIF or .JPEG format.
4) The neural networks trained with the data saved inside the "Nets" folder.
The original light data are produced using Hydrolight 5.2 which produced two separate datasets. After transformations (application of noise as described inside the paper), the data required to produce the results are saved inside the file modelledData.m.
The single MATLAB script uses these datasets to produce any of the image shown inside the publication.
The Landsat image and the coordinates of the different stations where SIOPs were collected are also present.
1) The MATLAB scripts "PlotFigures.m" and "metricsProper.m" to reproduce the different figures shown in the publication.
2) The datasets used for the creation of the figures are saved inside "modelledData.m", "SIOPS.xls", "InSitu_Stations_Coordinates.csv" and the "LandsatImage.TIFF".
3) The figures inside the "Figures" folder in .TIF or .JPEG format.
4) The neural networks trained with the data saved inside the "Nets" folder.
The original light data are produced using Hydrolight 5.2 which produced two separate datasets. After transformations (application of noise as described inside the paper), the data required to produce the results are saved inside the file modelledData.m.
The single MATLAB script uses these datasets to produce any of the image shown inside the publication.
The Landsat image and the coordinates of the different stations where SIOPs were collected are also present.
| Date made available | 31 Jan 2023 |
|---|---|
| Publisher | University of Strathclyde |
Research output
- 1 Article
-
Neural networks to retrieve in water constituents applied to radiative transfer models simulating coastal water conditions
Hadjal, M., Paterson, R. & McKee, D., 16 Feb 2023, In: Frontiers in Remote Sensing Multi- and Hyper-Spectral Imaging. 4, 16 p., 973944.Research output: Contribution to journal › Article › peer-review
Open AccessFile56 Downloads (Pure)
Projects
- 1 Finished
-
Artificial Light Impacts on Coastal Ecosystems (ALICE)
McKee, D. (Principal Investigator)
NERC (Natural Environment Research Council)
13/02/19 → 12/02/23
Project: Research