UBathy (v2.0): A Software to Obtain the Bathymetry from Video Imagery
Abstract
:1. Introduction
2. Methods and Software Description
- Software characteristics. UBathy is an open source software developed in (also open source) Python3 and available at GitHub platform. It runs on any platform with a standard installation of Python3.9 (i.e., with “The Python Standard Library”) and OpenCV, NumPy, SciPy and matplotlib modules. The code is complemented with an example case so that any user can fully execute it. A guided example is included to run on Jupyter Notebook to facilitate its use to non-experienced users. The software is suitable for users who are familiar with coastal image processing.
- Videos sources. A novel aspect of UBathy is that allows to directly process videos of raw camera images in addition to usual planview videos. In the first case (raw images), the videos can be acquired typically from fixed video monitoring stations (“Argus” type, [14]) but also from new CoastSnap stations [21]. For their processing, only the calibration of the camera is needed, without any further post-processing, together with the position of the mean water level at the time of the recording. Videos of planviews can normally come from fixed video monitoring stations or from the processing of drone flights. For planview videos it is necessary to know their georeference and the position of the mean water level.
- Mode decomposition. The extraction of the wave modes and corresponding wave periods is performed by global analysis of the images. The software allows, at user’s request, to decompose the videos in time (wave periods) and space (wave phase) using EOF, following [12], or DMD [13]. In addition, the user can also reduce the signal noise of the videos by applying an RPCA [19]. The wavenumber estimation uses local adjustments of the wave phase.
- Bathymetry estimation. The bathymetry is obtained by fitting the surface waves dispersion relationship with the wavenumbers and frequencies of the different modes, taking into account the different mean water levels. Another novel aspect of this software is the flexibility to obtain the bathymetry by assembling videos that: (1) cover different parts of the coastal area, (2) do not need to be recorded synchronously and with the same sea level and, (3) are made of different types of images (planviews and raw images). In addition to the bathymetry obtained from the above analysis, a Kalman filter is used to obtain the bathymetry at a given time by accumulating the results obtained up to that time.
- Step 0:
- Video and data setup. The video frames, data for image georeferencing and mean water level during the recording of the videos are provided.
- Step 1:
- Generation of meshes. Generation of the spatial meshes to express the mode decomposition, the wavenumbers and the bathymetry.
- Step 2:
- Mode decomposition. The wave is decomposed into different modes, with the corresponding wave period and spatial phase.
- Step 3:
- Wavenumber computation. The spatial phase of each mode is analyzed to extract the wavenumbers in the spatial domain.
- Step 4:
- Bathymetry estimation. Estimation of the bathymetry from the set of periods and wavenumbers of different videos.
- Step 5:
- Kalman filtering. Determination of the bathymetry evolution by time filtering the bathymetry results obtained from videos at different times.
2.1. Methodological Background
2.1.1. Generation of Meshes
2.1.2. Mode Decomposition
2.1.3. Wavenumber Computation
2.1.4. Bathymetry Estimation
2.1.5. Kalman Filtering
2.2. Software Implementation
2.2.1. Video and Data Setup
2.2.2. Generation of Meshes
2.2.3. Mode Decomposition
2.2.4. Wavenumber Computation
2.2.5. Bathymetry Estimation
2.2.6. Kalman Filtering
3. Results and Discussion
3.1. Bathymetry Estimation
3.1.1. Generation of Meshes
3.1.2. Mode Decomposition
3.1.3. Wavenumber Computation
3.1.4. Bathymetry Estimation
3.2. Kalman Filtering
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
EOF | Empirical orthogonal functions |
DMD | Dynamic mode decomposition |
FFT | Fast Fourier transform |
RANSAC | Random sample consensus approach |
RPCA | Robust principal component algorithm |
Appendix A. Software Source Code
- Software name: UBathy
- Developers: Gonzalo Simarro, Daniel Calvete
- Contact address:[email protected]
- Cost: free
- License: Creative Commons Attribution 4.0 International
- Availability:https://doi.org/10.5281/zenodo.7360216
- Year first available: 2022
- New developments:https://github.com/Ulises-ICM-UPC/UBathy (accessed on 10 November 2022)
- Hardware requirements: PC, server.
- System requirements: Windows, Linux, Mac.
- Program language: Python (3.9)
- Dependencies: OpenCV, NumPy, SciPy and matplotlib modules.
- Program size: 100 KB
- Documentation: README in GitHub repository and example in an editable Jupyter Notebook.
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Variable | parameters.json | Unit | Example | Ranges | |
---|---|---|---|---|---|
generation of meshes | delta_M | m | - | ||
delta_K | m | - | |||
delta_B | m | - | |||
mode decomposition | time_step | s | 30 | [1–60] | |
time_windows | s | [30–150] | |||
min_period | s | 3 | - | ||
max_period | s | 15 | - | ||
candes_iter | - | 50 | [40–80] | ||
- | DMD_or_EOF | - | “DMD” | “DMD” or “EOF” | |
DMD_rank | - | 6 | [5–10] | ||
EOF_variance | - | [0.010–0.100] | |||
wavenumber computation | min_depth | m | - | ||
max_depth | m | - | |||
nRadius_K | - | 3 | [2–5] | ||
cRadius_K | - | [0.40–0.60] | |||
nRANSAC_K | - | 50 | [25–100] | ||
bathymetry estimation | stdGammaC | - | [0.050–0.150] | ||
cRadius_B | - | [0.10–0.30] | |||
Kalman filtering | Kalman_ini | yyyyMMddhhmm | 202007250800 | - | |
Kalman_fin | yyyyMMddhhmm | 202008010900 | - | ||
Q | var_per_day | m/day | [0.05–0.25] |
Videos of 160 s | Videos of 600 s | |||||
---|---|---|---|---|---|---|
Case | Points | Biasm | RMSEm | Points | Biasm | RMSEm |
all | 5609 | 6174 | ||||
only | 5270 | 6134 | ||||
only | 5282 | 6026 | ||||
only | 5105 | 5977 |
Videos of 160 s | |||
---|---|---|---|
Case | Points | Bias m | RMSE m |
RANSAC, | 5609 | ||
no RANSAC, | 4420 | ||
no RANSAC, | 5427 | ||
no RANSAC, | 5989 | ||
no RANSAC, | 4502 |
Videos of 160 s | Videos of 600 s | |||||
---|---|---|---|---|---|---|
Case | Points | Biasm | RMSEm | Points | Biasm | RMSEm |
no Kalman | 4119 | 4429 | ||||
1 day | 4346 | 4469 | ||||
2 days | 4364 | 4477 | ||||
3 days | 4394 | 4480 | ||||
4 days | 4394 | 4480 | ||||
5 days | 4412 | 4488 | ||||
6 days | 4419 | 4493 | ||||
7 days | 4419 | 4493 |
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Share and Cite
Simarro, G.; Calvete, D. UBathy (v2.0): A Software to Obtain the Bathymetry from Video Imagery. Remote Sens. 2022, 14, 6139. https://doi.org/10.3390/rs14236139
Simarro G, Calvete D. UBathy (v2.0): A Software to Obtain the Bathymetry from Video Imagery. Remote Sensing. 2022; 14(23):6139. https://doi.org/10.3390/rs14236139
Chicago/Turabian StyleSimarro, Gonzalo, and Daniel Calvete. 2022. "UBathy (v2.0): A Software to Obtain the Bathymetry from Video Imagery" Remote Sensing 14, no. 23: 6139. https://doi.org/10.3390/rs14236139