Development of a Fully Convolutional Neural Network to Derive Surf-Zone Bathymetry from Close-Range Imagery of Waves in Duck, NC
Abstract
:1. Introduction
2. Data Sources
3. Methodology
3.1. Neural Network Model
3.2. Experiment Workflow
4. Results
5. Discussion
5.1. Comparison to Optical Wave-Based Inversion Methods
5.2. Activation Maps
5.3. Wave Conditions
5.4. Transfer Learning
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Wilson, G.W.; Özkan-Haller, H.T.; Holman, R.A. Data assimilation and bathymetric inversion in a two-dimensional horizontal surf zone model. J. Geophys. Res. Ocean. 2010, 115. [Google Scholar] [CrossRef]
- Plant, N.G.; Edwards, K.L.; Kaihatu, J.M.; Veeramony, J.; Hsu, L.; Holland, K.T. The effect of bathymetric filtering on nearshore process model results. Coast. Eng. 2009, 56, 484–493. [Google Scholar] [CrossRef]
- Sallenger, A.H., Jr.; Holman, R.A.; Birkemeier, W.A. Storm-induced response of a nearshore-bar system. Mar. Geol. 1985, 64, 237–257. [Google Scholar] [CrossRef]
- Holland, K.T.; Holman, R.A. Field observations of beach cusps and swash motions. Mar. Geol. 1996, 134, 77–93. [Google Scholar] [CrossRef]
- Birkemeier, W.A.; Mason, C. The CRAB: A unique nearshore surveying vehicle. J. Surv. Eng. 1984, 110, 1–7. [Google Scholar] [CrossRef]
- Fredsøe, J.; Deigaard, R. Mechanics of Coastal Sediment Transport; World Scientific: Singapore, 1992; Volume 3. [Google Scholar]
- Jackson, D.W.T.; Cooper, J.A.G.; Del Rio, L. Geological control of beach morphodynamic state. Mar. Geol. 2005, 216, 297–314. [Google Scholar] [CrossRef]
- Gao, J. Bathymetric mapping by means of remote sensing: Methods, accuracy and limitations. Prog. Phys. Geogr. 2009, 33, 103–116. [Google Scholar] [CrossRef]
- Holman, R.A.; Stanley, J. The history and technical capabilities of Argus. Coast. Eng. 2007, 54, 477–491. [Google Scholar] [CrossRef]
- Brodie, K.L.; Bruder, B.L.; Slocum, R.K.; Spore, N.J. Simultaneous Mapping of Coastal Topography and Bathymetry from a Lightweight Multicamera UAS. IEEE Trans. Geosci. Remote Sens. 2019, 57, 6844–6864. [Google Scholar] [CrossRef]
- Almar, R.; Bergsma, E.W.J.; Maisongrande, P.; de Almeida, L.P.M. Wave-derived coastal bathymetry from satellite video imagery: A showcase with Pleiades persistent mode. Remote Sens. Environ. 2019, 231, 111263. [Google Scholar] [CrossRef]
- Bergsma, E.W.J.; Almar, R.; Maisongrande, P. Radon-Augmented Sentinel-2 Satellite Imagery to Derive Wave-Patterns and Regional Bathymetry. Remote Sens. 2019, 11, 1918. [Google Scholar] [CrossRef] [Green Version]
- Vos, K.; Harley, M.D.; Splinter, K.D.; Simmons, J.A.; Turner, I.L. Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery. Coast. Eng. 2019, 150, 160–174. [Google Scholar] [CrossRef]
- Legleiter, C.J.; Roberts, D.A.; Lawrence, R.L. Spectrally based remote sensing of river bathymetry. Earth Surf. Process. Landf. 2009, 34, 1039–1059. [Google Scholar] [CrossRef]
- Eugenio, F.; Marcello, J.; Martin, J. High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 2015, 53, 3539–3549. [Google Scholar] [CrossRef]
- Jagalingam, P.; Akshaya, B.J.; Hegde, A.V. Bathymetry mapping using Landsat 8 satellite imagery. Procedia Eng. 2015, 116, 560–566. [Google Scholar] [CrossRef] [Green Version]
- Pacheco, A.; Horta, J.; Loureiro, C.; Ferreira, Ó. Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters. Remote Sens. Environ. 2015, 159, 102–116. [Google Scholar] [CrossRef] [Green Version]
- Jawak, S.D.; Vadlamani, S.S.; Luis, A.J. A Synoptic Review on Deriving Bathymetry Information Using Remote Sensing Technologies: Models, Methods and Comparisons. Adv. Remote Sens. 2015, 4, 147–162. [Google Scholar] [CrossRef] [Green Version]
- Jay, S.; Guillaume, M. Regularized estimation of bathymetry and water quality using hyperspectral remote sensing. Int. J. Remote Sens. 2016, 37, 263–289. [Google Scholar] [CrossRef]
- Lin, Y.C.; Cheng, Y.T.; Zhou, T.; Ravi, R.; Hasheminasab, S.M.; Flatt, J.E.; Troy, C.; Habib, A. Evaluation of UAV LiDAR for mapping coastal environments. Remote Sens. 2019, 11, 2893. [Google Scholar] [CrossRef] [Green Version]
- Vahtmäe, E.; Paavel, B.; Kutser, T. How much benthic information can be retrieved with hyperspectral sensor from the optically complex coastal waters? J. Appl. Remote Sens. 2020, 14, 16504. [Google Scholar] [CrossRef]
- Ashphaq, M.; Srivastava, P.K.; Mitra, D. Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research. J. Ocean. Eng. Sci. 2021. [Google Scholar] [CrossRef]
- Niroumand-Jadidi, M.; Vitti, A.; Lyzenga, D.R. Multiple Optimal Depth Predictors Analysis (MODPA) for river bathymetry: Findings from spectroradiometry, simulations, and satellite imagery. Remote Sens. Environ. 2018, 218, 132–147. [Google Scholar] [CrossRef]
- Niroumand-Jadidi, M.; Bovolo, F.; Bruzzone, L. SMART-SDB: Sample-specific multiple band ratio technique for satellite-derived bathymetry. Remote Sens. Environ. 2020, 251, 112091. [Google Scholar] [CrossRef]
- Patel, A.; Katiyar, S.K.; Prasad, V. Bathymetric Mapping of Bhopal City Lower Lake Using IRS-P6: LISS-4 Imagery and Artificial Neural Network Technique. J. Indian Soc. Remote Sens. 2016, 44, 605–616. [Google Scholar] [CrossRef]
- Wei, S.; Qian, J.; Yali, R.; Ran, M. Comparative Study of Different Machine Learning Models for Remote Sensing Bathymetry Inversion. In Proceedings of the International Conference on Intelligent and Fuzzy Systems, Istanbul, Turkey, 21–23 July 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 1140–1148. [Google Scholar]
- Melsheimer, C.; Chin, L.S. Extracting bathymetry from multi-temporal SPOT images. In Proceedings of the 22nd Asian Conference on Remote Sensing, Singapore, 5–9 November 2001; Volume 9. [Google Scholar]
- Evagorou, E.; Mettas, C.; Agapiou, A.; Themistocleous, K.; Hadjimitsis, D. Bathymetric maps from multi-temporal analysis of Sentinel-2 data: The case study of Limassol, Cyprus. Adv. Geosci. 2019, 45, 397–407. [Google Scholar] [CrossRef] [Green Version]
- Sagawa, T.; Yamashita, Y.; Okumura, T.; Yamanokuchi, T. Satellite derived bathymetry using machine learning and multi-temporal satellite images. Remote Sens. 2019, 11, 1155. [Google Scholar] [CrossRef] [Green Version]
- Misra, A.; Ramakrishnan, B. Assessment of coastal geomorphological changes using multi-temporal Satellite-Derived Bathymetry. Cont. Shelf Res. 2020, 207, 104213. [Google Scholar] [CrossRef]
- Niroumand-Jadidi, M.; Bovolo, F.; Bruzzone, L.; Gege, P. Physics-based bathymetry and water quality retrieval using planetscope imagery: Impacts of 2020 COVID-19 lockdown and 2019 extreme flood in the Venice Lagoon. Remote Sens. 2020, 12, 2381. [Google Scholar] [CrossRef]
- Lippmann, T.C.; Holman, R.A. The spatial and temporal variability of sand bar morphology. J. Geophys. Res. Ocean. 1990, 95, 11575–11590. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Roy, D.P.; Wulder, M.A.; Loveland, T.R.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Helder, D.; Irons, J.R.; Johnson, D.M.; Kennedy, R.; et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 2014, 145, 154–172. [Google Scholar] [CrossRef] [Green Version]
- Lippmann, T.C.; Holman, R.A. Quantification of sand bar morphology: A video technique based on wave dissipation. J. Geophys. Res. Ocean. 1989, 94, 995–1011. [Google Scholar] [CrossRef]
- Holland, K.T.; Holman, R.A.; Lippmann, T.C.; Stanley, J.; Plant, N. Practical use of video imagery in nearshore oceanographic field studies. IEEE J. Ocean. Eng. 1997, 22, 81–92. [Google Scholar] [CrossRef]
- Aarninkhof, S.G.J.; Holman, R.A. Monitoring the nearshore with video. Backscatter 1999, 10, 8–11. [Google Scholar]
- Aarninkhof, S.G.J.; Turner, I.L.; Dronkers, T.D.T.; Caljouw, M.; Nipius, L. A video-based technique for mapping intertidal beach bathymetry. Coast. Eng. 2003, 49, 275–289. [Google Scholar] [CrossRef]
- Aarninkhof, S.G.J. Nearshore Bathymetry Derived from Video Imagery. Ph.D. Thesis, Delft University, Delft, The Netherlands, 2003. [Google Scholar]
- Aarninkhof, S.G.J.; Ruessink, B.G.; Roelvink, J.A. Nearshore subtidal bathymetry from time-exposure video images. J. Geophys. Res. Ocean. 2005, 110. [Google Scholar] [CrossRef]
- Van Dongeren, A.; Plant, N.; Cohen, A.; Roelvink, D.; Haller, M.C.; Catalán, P. Beach Wizard: Nearshore bathymetry estimation through assimilation of model computations and remote observations. Coast. Eng. 2008, 55, 1016–1027. [Google Scholar] [CrossRef]
- Stockdon, H.F.; Holman, R.A. Estimation of wave phase speed and nearshore bathymetry from video imagery. J. Geophys. Res. Ocean. 2000, 105, 22015–22033. [Google Scholar] [CrossRef]
- Plant, N.G.; Holland, K.T.; Haller, M.C. Ocean wavenumber estimation from wave-resolving time series imagery. IEEE Trans. Geosci. Remote Sens. 2008, 46, 2644–2658. [Google Scholar] [CrossRef]
- Holman, R.; Plant, N.; Holland, T. cBathy: A robust algorithm for estimating nearshore bathymetry. J. Geophys. Res. Ocean. 2013, 118, 2595–2609. [Google Scholar] [CrossRef]
- Bergsma, E.W.J.; Almar, R. Video-based depth inversion techniques, a method comparison with synthetic cases. Coast. Eng. 2018, 138, 199–209. [Google Scholar] [CrossRef]
- Wilson, G.W.; Özkan-Haller, H.T.; Holman, R.A.; Haller, M.C.; Honegger, D.A.; Chickadel, C.C. Surf zone bathymetry and circulation predictions via data assimilation of remote sensing observations. J. Geophys. Res. 2014, 119, 1993–2016. [Google Scholar] [CrossRef] [Green Version]
- Brodie, K.L.; Palmsten, M.L.; Hesser, T.J.; Dickhudt, P.J.; Raubenheimer, B.; Ladner, H.; Elgar, S.J.C.E. Evaluation of video-based linear depth inversion performance and applications using altimeters and hydrographic surveys in a wide range of environmental conditions. Coast. Eng. 2018, 136, 147–160. [Google Scholar] [CrossRef] [Green Version]
- Bergsma, E.W.J.; Conley, D.C.; Davidson, M.A.; O’Hare, T.J.; Almar, R. Storm event to seasonal evolution of nearshore bathymetry derived from shore-based video imagery. Remote Sens. 2019, 11, 519. [Google Scholar] [CrossRef] [Green Version]
- Bak, A.S.; Brodie, K.L.; Hesser, T.J.; Smith, J.M. Applying dynamically updated nearshore bathymetry estimates to operational nearshore wave modeling. Coast. Eng. 2019, 145, 53–64. [Google Scholar]
- Mandlburger, G. Bathymetry from Active and Passive Airborne Remote Sensing—Looking Back and Ahead. 2017. Available online: https://phowo.ifp.uni-stuttgart.de/2017/PDF/23-Mandlburger-Abstract.pdf (accessed on 1 October 2021).
- Dickens, K.; Armstrong, A. Application of machine learning in satellite derived bathymetry and coastline detection. SMU Data Sci. Rev. 2019, 2, 4. [Google Scholar]
- Yunus, A.P.; Dou, J.; Song, X.; Avtar, R. Improved bathymetric mapping of coastal and lake environments using Sentinel-2 and Landsat-8 images. Sensors 2019, 19, 2788. [Google Scholar] [CrossRef] [Green Version]
- Wilson, B.; Kurian, N.C.; Singh, A.; Sethi, A. Satellite-Derived Bathymetry Using Deep Convolutional Neural Network. In Proceedings of the IGARSS 2020—2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 19–24 July 2020; pp. 2280–2283. [Google Scholar]
- Ai, B.; Wen, Z.; Wang, Z.; Wang, R.; Su, D.; Li, C.; Yang, F. Convolutional neural network to retrieve water depth in marine shallow water area from remote sensing images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 2888–2898. [Google Scholar] [CrossRef]
- Lumban-Gaol, Y.A.; Ohori, K.A.; Peters, R.Y. Satellite-derived bathymetry using convolutional neural networks and multispectral sentinel-2 images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, 43, 201–207. [Google Scholar] [CrossRef]
- Mandlburger, G.; Kölle, M.; Nübel, H.; Soergel, U. BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images. PFG—J. Photogramm. Remote. Sens. Geoinf. Sci. 2021, 1–19. [Google Scholar] [CrossRef]
- de Silva, A.; Mori, I.; Dusek, G.; Davis, J.; Pang, A. Automated rip current detection with region based convolutional neural networks. Coast. Eng. 2021, 166, 103859. [Google Scholar] [CrossRef]
- Buscombe, D.; Carini, R.J.; Harrison, S.R.; Chickadel, C.C.; Warrick, J.A. Optical wave gauging using deep neural networks. Coast. Eng. 2020, 155, 103593. [Google Scholar] [CrossRef]
- Buscombe, D.; Ritchie, A.C. Landscape classification with deep neural networks. Geosciences 2018, 8, 244. [Google Scholar] [CrossRef] [Green Version]
- Ellenson, A.; Simmons, J.; Wilson, G.; Hesser, T.; Splinter, K.D. Machine Learning Classification of Beach State from Argus Imagery. Coast. Eng. Proc. 2020, 37. [Google Scholar] [CrossRef]
- Benshila, R.; Thoumyre, G.; Najar, M.A.; Abessolo, G.; Almar, R. A Deep Learning Approach for Estimation of the Nearshore Bathymetry A Deep Learning Approach for Estimation of the Nearshore. J. Coast. Res. 2020, 95, 1011–1015. [Google Scholar] [CrossRef]
- Collins, A.; Brodie, K.L.; Bak, S.; Hesser, T.; Farthing, M.W.; Gamble, D.W.; Long, J.W. A 2D Fully Convolutional Neural Network for Nearshore and Surf-Zone Bathymetry Inversion from Synthetic Imagery of Surf-Zone using the Model Celeris. In Proceedings of the AAAI Spring Symposium: MLPS, Stanford, CA, USA, 23–25 March 2020. [Google Scholar]
- Collins, A.M.; Brodie, K.L.; Bak, A.S.; Hesser, T.J.; Farthing, M.W.; Lee, J.; Long, J.W. Bathymetric Inversion and Uncertainty Estimation from Synthetic Surf-Zone Imagery with Machine Learning. J. Remote. Sens. 2020, 12, 3364. [Google Scholar] [CrossRef]
- Al Najar, M.; Thoumyre, G.; Bergsma, E.W.J.; Almar, R.; Benshila, R.; Wilson, D.G. Satellite derived bathymetry using deep learning. Mach. Learn. 2021, 1–24. [Google Scholar] [CrossRef]
- Tavakkol, S.; Lynett, P. Celeris: A GPU-accelerated open source software with a Boussinesq-type wave solver for real-time interactive simulation and visualization. Comput. Phys. Commun. 2017, 217, 117–127. [Google Scholar] [CrossRef] [Green Version]
- Long, C.E.; Oltman-Shay, J.M. Directional Characteristics of Waves in Shallow Water (No. CERC-TR-91-1); Coastal Engineering Research Center: Vicksburg, MS, USA, 1991. [Google Scholar]
- Splinter, K.D.; Holman, R.A. Bathymetry estimation from single-frame images of nearshore waves. IEEE Trans. Geosci. Remote Sens. 2009, 47, 3151–3160. [Google Scholar] [CrossRef] [Green Version]
Error Statistics over the Test Set | |||
---|---|---|---|
Depth Bias (m) | MAE (m) | RMSE (m) | 90% Error (m) |
0.06 | 0.31 | 0.37 | 0.79 |
Wave Conditions for Survey Time Series | ||||
---|---|---|---|---|
Date | Hs (m) | T (s) | D (°) | Time |
2019-02-04 17 | 2.01 | 8.83 | 110 | 2 |
2019-03-26 15 | 3.18 | 7.69 | 41 | 1 |
2019-04-10 15 | 0.85 | 4.11 | 55 | 1 |
2019-04-13 16 | 1.21 | 8.25 | 114 | 2 |
2019-06-28 18 | 0.35 | 8.33 | 107 | 3 |
2019-09-11 18 | 0.80 | 11.11 | 80 | 2 |
2019-11-14 16 | 0.87 | 7.14 | 66 | 1 |
2019-12-10 18 | 2.25 | 9.10 | 108 | 3 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Collins, A.M.; Geheran, M.P.; Hesser, T.J.; Bak, A.S.; Brodie, K.L.; Farthing, M.W. Development of a Fully Convolutional Neural Network to Derive Surf-Zone Bathymetry from Close-Range Imagery of Waves in Duck, NC. Remote Sens. 2021, 13, 4907. https://doi.org/10.3390/rs13234907
Collins AM, Geheran MP, Hesser TJ, Bak AS, Brodie KL, Farthing MW. Development of a Fully Convolutional Neural Network to Derive Surf-Zone Bathymetry from Close-Range Imagery of Waves in Duck, NC. Remote Sensing. 2021; 13(23):4907. https://doi.org/10.3390/rs13234907
Chicago/Turabian StyleCollins, Adam M., Matthew P. Geheran, Tyler J. Hesser, Andrew Spicer Bak, Katherine L. Brodie, and Matthew W. Farthing. 2021. "Development of a Fully Convolutional Neural Network to Derive Surf-Zone Bathymetry from Close-Range Imagery of Waves in Duck, NC" Remote Sensing 13, no. 23: 4907. https://doi.org/10.3390/rs13234907