Satellite-Derived Bathymetry Using Deep Convolutional Neural Network

Wilson, Bibin; Kurian, Nikhil Cherian; Singh, Anand; Sethi, Amit

doi:10.1109/igarss39084.2020.9324053

Cited by 10 publications

(9 citation statements)

References 16 publications

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“…Several machine learning algorithms, such as multivariate regression, support vector machine (SVM), K-nearest neighbor (KNN), random forest [14], [16]- [20] have also been considered. Following the recent adoption and success of deep learning in image processing and other areas, basic fully-connected feedforward neural networks (FCFFNN) [16], [21], [22], convolutional neural networks (CNN) [23]- [29], gated recurrent unit (GRU) networks [30] and hybrid models where particle swarm optimization (PSO) and optimally pruned extreme learning machine (OPELM) are combined with neural networks, have been used for achieving better performance over the classical and machine learning models. Most of the works consider only satellite data pre-processed using different techniques as the input features.…”

Section: A Literature Surveymentioning

confidence: 99%

“…Most of the above mentioned models for SDB are supervised learning based models, where the models are trained to estimate depth of water columns from satellite data/images using certain ground truth data. The works use depth information collected from acoustic systems [11]- [13], [15], [16], [20]- [23], [31]- [35], airborne LiDAR systems [10], [11], [14], [17], [19], [26], [28], [36]- [38] or LiDAR data obtained from Ice, Cloud, and Elevation Satellite-2 (ICESat-2) satellites [18], [29], [30], as ground truth data. The study areas of interest in most of the above works belong to coastal regions [10]- [12], [14], [15], [17]- [19], [21], [28], [32], [34], [37], [39] and only a small number of works study SDB for inland water bodies [13], [16], [38].…”

Section: A Literature Surveymentioning

confidence: 99%

“…In this work, we are interested in carrying out SDB for shallow-depth inland water bodies using deep learning methods, for which we use band reflectance values provided by Sentinel-2 satellites as features and acoustic bathymetry data as ground truth data. From the above, it can be seen that only a very few works (for instance, [16], [23], [27], [35]) consider inland water bathymetry with deep learning models. Among them, [16] obtains random forest based algorithm for SDB, [35] obtains neural network models for river bathymetry using imagery from three different satellites, namely, Landsat-9 (30 meter resolution), Sentinel-2 (10 meter resolution), and SuperDoves (3 meter resolution) and [23] obtains deep convolutional network based models for SDB, where authors cast the problem of SDB as a pixel-wise classification task.…”

Section: A Literature Surveymentioning

confidence: 99%

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Satellite-Derived Bathymetry of an Inland Reservoir in India

Gupta¹,

Bhat²,

Balan³

2023

Preprint

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Estimating depth of water columns in reservoirs, referred to as bathymetry, is an important task in water resource management. Satellite-derived bathymetry (SDB), where depths of water columns are estimated using satellite data, is an emerging and promising technique, especially for shallow-water scenarios. In this work, our objective is to obtain deep-learning models for SDB of a shallow water reservoir in Western India using supervised learning. For accomplishing this, after a series of pre-processing steps, we generate training data consisting of band reflectance values provided by Sentinel-2 satellite as input features and corresponding depth values collected by acoustic bathymetry as the ground truth data. The main contributions of the work include obtaining deep learning models that perform better than other classical techniques in terms of estimation accuracy, towards which we first obtain one dimensional (1D) convolutional neural network (CNN) and 2D-CNN models, which estimate depth using satellite data in tabular and image formats respectively. We then propose a hybrid model where we first classify each pixel of the image to correspond to high or low depth values and then perform regression on each of them separately. From the study, we show that (i) considering all the available bands of the Sentinel-2 satellites or those having high correlation with the ground truth depths gives significant performance improvement over considering only a subset of the bands having resolution of 10 meters, (ii) the 2D-CNN model trained using the image dataset yields significant performance improvement compared to other models trained on tabular data, (iii) and that the hybrid model which uses 2D-CNN for both the classification and regression tasks, trained on image data, performs best among all the models.

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Section: A Literature Surveymentioning

confidence: 99%

Section: A Literature Surveymentioning

confidence: 99%

Section: A Literature Surveymentioning

confidence: 99%

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Satellite-Derived Bathymetry of an Inland Reservoir in India

Gupta¹,

Bhat²,

Balan³

2023

Preprint

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“…In [37], DL is used to estimate seabed depth based on the radiative transfer of light in water in multispectral images from the Orbview-3 satellite. In [38], a CNN is used to estimate depths of the Devils Lake Area (ND, USA), casting estimation as a classification problem with classes at each foot of depth. The most convincing application of deep learning to coastal SDB currently appears to be from [39], which uses reflectance values from Sentinel-2 Level 2A images to estimate coastal water depth with high precision in clear waters (1.48 m RMSE).…”

Section: Introductionmentioning

confidence: 99%

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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“…Due to this factor, there are multiple studies that derive field measurements from remote sensing data instead of manual field data collection. This includes air quality measurements using satellite-derived particulate matter measurements [2], rainfall observation [3], bathymetry [4], climatology [5], chlorophyll and algae bloom indices [6], land surface temperature [7], etc.…”

Section: Introductionmentioning

confidence: 99%

Deriving Surface Resistivity from Polarimetric SAR Data Using Dual-Input UNet

Wilson¹,

Kumar²,

Bhogapurapu³

et al. 2022

Preprint

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Traditional survey methods for finding surface re- sistivity are time consuming and labor intensive. Very few studies have focused on finding the resistivity / conductivity using remote sensing data and deep learning techniques. In this line of work, we assessed the correlation between surface resistivity and Synthetic Aperture Radar (SAR) by applying various deep learning methods and tested our hypothesis in the Coso Geothermal Area, USA. For detecting the resistivity, L- band full polarimetric SAR data acquired by UAVSAR were used, and MT (Magnetotellurics) inverted resistivity data of the area were used as the ground truth. We conducted experiments to compare various deep learning architectures, and suggest the use of Dual Input UNet (DI-UNet) architecture. DI-UNet uses a deep learning architecture to predict the resistivity using full polarimetric SAR data by promising a quick survey addition to the traditional method. Our proposed approach accomplished improved outcomes for the mapping of MT resistivity from SAR data.

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Satellite-Derived Bathymetry Using Deep Convolutional Neural Network

Cited by 10 publications

References 16 publications

Satellite-Derived Bathymetry of an Inland Reservoir in India

Satellite-Derived Bathymetry of an Inland Reservoir in India

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

Deriving Surface Resistivity from Polarimetric SAR Data Using Dual-Input UNet

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