Hybrid Artificial Neural Networks for Modeling Shallow-Water Bathymetry via Satellite Imagery

Kaloop, Mosbeh R.; El-Diasty, Mohammed; Hu, Jong Wan; Zarzoura, Fawzi

doi:10.1109/tgrs.2021.3107839

Cited by 18 publications

(10 citation statements)

References 53 publications

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“…In Table III, we present results obtained when the depth values are estimated using different models. For understanding the impact and contribution of different band values in depth estimation of deep learning models, we consider data where all the bands values, only the band values which are highly correlated with depth values, and only the four commonly-used bands with 10 meter resolution are used, as in [14], [16], [20], [21]. We also evaluate the performance with different metrics.…”

Section: Results: Experiments Performance Comparison and Discussionmentioning

confidence: 99%

“…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%

“…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]. As mentioned, most of the above works consider the reflectance values collected by satellites as input for estimating depth of water columns.…”

Section: A Literature Surveymentioning

confidence: 99%

“…In the existing works, the trained models are evaluated on the same region in [11], [13], [21], [23], [30], [32]- [34], [37], [38] and different nearby regions in [10], [12], [14]- [19], [26], [28], [29], [35], [36]. We evaluate the proposed models in the same region, as in multiple works above.…”

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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<p>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 <em>high </em>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. </p>

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Section: Results: Experiments Performance Comparison and Discussionmentioning

confidence: 99%

Section: A Literature Surveymentioning

confidence: 99%

Section: A Literature Surveymentioning

confidence: 99%

Section: A Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

Satellite-Derived Bathymetry of an Inland Reservoir in India

Gupta¹,

Bhat²,

Balan³

2023

Preprint

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“…If ten years ago, the primary goal of the research on submerged topography was to understand the relation between the water column reflectance and the water depth using statistical and trigonometrical models, the spread of artificial intelligence that allows users to investigate the non-linear and very complex relationship between variables, has given a new light of interest on spectral-based bathymetry. (Wang et al, 2007) are the forerunners of neural networks applied to water depth studies, followed closely by much other research based on satellite data (Makboul et al, 2017;Kaloop et al, 2021) and UAV (Slocum et al, 2020). Although these efforts provided good results, there are some concerns regarding the applicability of ML for bathymetry problems since the performance of learning-based techniques can be only as good as the training data.…”

Section: Introductionmentioning

confidence: 99%

Laying the Foundation for an Artificial Neural Network for Photogrammetric Riverine Bathymetry

Belcore

Pietra

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This work aims to test the effectiveness of artificial intelligence for correcting water refraction in shallow inland water using very high-resolution images collected by Unmanned Aerial Systems (UAS) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electromagnetic spectrum. An artificial neural network is created using data of three morphologically similar alpine rivers. The RGB information, the SfM depth and seven radiometric indices are calculated and stacked in an 11-bands raster (input dataset). The depths are calculated as the difference between the Up component of the bathymetry cross-sections and the water surface quotas and constitute the dependent variable of the regression. The dataset is then scaled. The observations of one of the analyzed case studies are used as the unseen dataset to test the generalization capability of the model. The remaining observations are divided into test (20%) and training (80%) datasets. The generated NN is a 3-layer MLP model with one hidden layer and the Rectified Linear Unit (ReLU) and sigmoid activation functions. The weights are initialized to small Gaussian random values, and kernel regularizers, L1 and L2, are added to reduce the overfitting. Weights are updated with the Adam search technique, and the mean squared error is the loss function. The importance and significance of 11 variables are assessed. The model has a 0.70 r-squared score on the test dataset and 0.77 on the training dataset. The MAE is 0.06 and the RMSE 0.08, similar results obtained from the unseen dataset. Although the good metrics, the model shows some difficulties generalizing swallow depths.

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