Satellite derived bathymetry using deep learning

Najar, Mahmoud Al; Thoumyre, Grégoire; Bergsma, Erwin W. J.; Almar, Rafaël; Benshila, Rachid; Wilson, Dennis G.

doi:10.1007/s10994-021-05977-w

Cited by 24 publications

(24 citation statements)

References 38 publications

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“…The Deep Single-Point Estimation of Bathymetry method [42] is a deep learning-based bathymetry inversion method that operates on 40 × 40 × 4 px multi-spectral input subtiles (corresponding to the blue (B2), green (B3), red (B4), and near-infrared (B8) bands of the Sentinel-2 satellite constellation [12] at 10 m resolution) to estimate the water depth corresponding to the center of each input subtile. The neural network input is an image of 40 × 40 × 4 px input channels conforming to our dataset of 40 × 40 × 4 satellite subtiles; and a single output neuron with a Rectified Linear Unit (ReLU) activation, corresponding to the average depth beneath the imaged area.…”

Section: Deep Single-point Estimation Of Bathymetrymentioning

confidence: 99%

“…Figure 1 presents the different steps of the DSPEB method. The deep learning parameters for DSPEB, including the choice of the model architecture and learning hyperparameters, were studied in a previous work [42]. We found that while networks of varying depth can perform bathymetry estimation, small convolutional neural networks (CNN) are sufficient.…”

Section: Deep Single-point Estimation Of Bathymetrymentioning

confidence: 99%

“…In this work, we apply the previously developed Deep Single-Point Estimation of Bathymetry (DSPEB) method [42] and showcase its ability to reconstruct bathymetry using real-world data. We create a supervised dataset for bathymetry inversion using publicly available Sentinel-2 imagery [12] and a number of bathymetry surveys obtained from the French Naval Hydrographic and Oceanographic Service (SHOM).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches

et al. 2022

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The ability to monitor the evolution of the coastal zone over time is an important factor in coastal knowledge, development, planning, risk mitigation, and overall coastal zone management. While traditional bathymetry surveys using echo-sounding techniques are expensive and time consuming, remote sensing tools have recently emerged as reliable and inexpensive data sources that can be used to estimate bathymetry using depth inversion models. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth observation and model inversion applications. In this work, we make use of publicly available Sentinel-2 satellite imagery and multiple bathymetry surveys to train a deep learning-based bathymetry estimation model. We explore for the first time two complementary approaches, based on color information but also wave kinematics, as inputs to the deep learning model. This offers the possibility to derive bathymetry not only in clear waters as previously done with deep learning models but also at common turbid coastal zones. We show competitive results with a state-of-the-art physical inversion method for satellite-derived bathymetry, Satellite to Shores (S2Shores), demonstrating a promising direction for worldwide applicability of deep learning models to inverse bathymetry from satellite imagery and a novel use of deep learning models in Earth observation.

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Section: Deep Single-point Estimation Of Bathymetrymentioning

confidence: 99%

Section: Deep Single-point Estimation Of Bathymetrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches

et al. 2022

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“…Adding bathymetry data, for instance acquired with LiDAR, can improve the accuracy of the results [88,156,[229][230][231]. It is possible to estimate bathymetry and water depth, with one of a numbered methods that currently exist [232][233][234][235], and to include this as an additional input to a coral reef mapping algorithm [236]. This method is found in Collin et al 2021 [39], where it improves the accuracy by up to 3%, allowing more than 98% overall accuracy with high-resolution WV-3 images.…”

Section: Additional Inputs To Coral Mappingmentioning

confidence: 99%

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

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Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

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“…Absent atmospheric correction, machine learning has been found to be superior over fittings to rigorous optical models [1]. Neural nets have been extensively used for remote sensing (RS), including convolutional neural nets (CNN), NN-physics hybrid methods [2], and to utilize multiple bands or spectra [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

Lawen

Salman

et al. 2022

Water

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The developed method extracts bathymetry distributions from multiple satellite image bands. The automated remote sensing function is sparsely coded and combines spiking neural net anomaly filtration, spline, and multi-band fittings. Survey data were used to identify an activation threshold, decay rate, spline fittings, and multi-band weighting factors. Errors were computed for remotely sensed Landsat satellite images. Multi-band fittings achieved an average error of 25.3 cm. This proved sufficiently accurate to automatically extract shorelines to eliminate land areas in bathymetry mapping.

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Satellite derived bathymetry using deep learning

Cited by 24 publications

References 38 publications

Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches

Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

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