Bathymetry Determination from High Resolution Satellite Imagery Using Ensemble Learning Algorithms in Shallow Lakes: Case Study El-Burullus Lake

Mohamed, Hassan; Negm, Abdelazim; Zahran, Mohamed; Saavedra, Oliver

doi:10.7763/ijesd.2016.v7.787

Cited by 36 publications

(21 citation statements)

References 28 publications

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“…Experiments there showed that the localized model reduced the bathymetry estimation error by 60% from an RMSE of 1.23m to 0.48m. In (Mohamed et al, 2016) a methodology is introduced using an Ensemble Learning (EL) fitting algorithm of Least Squares Boosting (LSB) for bathymetric maps calculation in shallow lakes from high resolution satellite images and water depth measurement samples using Echo-sounder. The retrieved bathymetric information from the three methods was evaluated using Echo Sounder data.…”

Section: Bathymetry Determination Using Machine Learningmentioning

confidence: 99%

Shallow Water Bathymetry Mapping From Uav Imagery Based on Machine Learning

Agrafiotis

Skarlatos

Georgopoulos

et al. 2019

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

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The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.

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Section: Bathymetry Determination Using Machine Learningmentioning

confidence: 99%

Shallow Water Bathymetry Mapping From Uav Imagery Based on Machine Learning

Agrafiotis

Skarlatos

Georgopoulos

et al. 2019

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

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“…In this section, the radiative transfer model of the water column is described in brief followed by methods to extend SBR (or NDR) to RDLC for the LCC estimation in multispectral satellite images. The main idea comes from the following two aspects: (1) based on previous studies on hyperspectral data, LCCs should correlate linearly to a particular SBR (or NDR); (2) according to the principle of band combination bathymetric methods [39][40][41][42][43], there is an approximate linear dependence between the water depth and the combination of different bands.…”

Section: Methodsmentioning

confidence: 99%

Method to design a live coral cover sensitive index for multispectral satellite images

Huang¹,

Yu²,

Wang³

et al. 2018

Opt. Express

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Live coral cover (LCC) is regarded as the most efficient indicator of coral reef health. However, LCCs are usually sampled with standardized transect or photo quadrat techniques in field, which are incomplete and labour-intensive. To overcome such difficulties, we study a model to transfer the pixels of multispectral satellite images to quantitative LCCs. The idea is to extend band ratio-based (BR) indices to a novel index constructed using the ratio of different linear combinations (RDLC) of band reflectance and water depths. On the basis of field surveyed LCCs, an empirical process is further proposed to solve the unknown parameters of this RDLC. This approach provides new thinking for designing LCC-sensitive indices for given multispectral satellite images. The experimental results on Weizhou Island and Palmyra Atoll demonstrate that the method is effective and feasible, where the mean relative errors (MREs) are improved from 45 to 56% for BRs to 23-29% for RDLCs for Weizhou Island.

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“…Most of those methods are based on the relationship between the reflectance and the depth. Some of them exploit an SVM system to predict the correct depth [45,46]; experiments therein showed that the localized model reduced the bathymetry estimation error by 60% from a Root Mean Square Error (RMSE) of 1.23 m to 0.48 m. In [47] a methodology is introduced using an ensemble learning (EL) fitting algorithm of least squares boosting (LSB) for bathymetric map calculations in shallow lakes from high resolution satellite images and water depth measurement samples using an echo sounder. The bathymetric information retrieved from the three methods [45][46][47] was evaluated using echo sounder data.…”

Section: Image-based Bathymetry Estimation Using Machine Learning Andmentioning

confidence: 99%

“…Some of them exploit an SVM system to predict the correct depth [45,46]; experiments therein showed that the localized model reduced the bathymetry estimation error by 60% from a Root Mean Square Error (RMSE) of 1.23 m to 0.48 m. In [47] a methodology is introduced using an ensemble learning (EL) fitting algorithm of least squares boosting (LSB) for bathymetric map calculations in shallow lakes from high resolution satellite images and water depth measurement samples using an echo sounder. The bathymetric information retrieved from the three methods [45][46][47] was evaluated using echo sounder data. The LSB fitting ensemble resulted in an RMSE of 0.15 m where the Principal Component Analysis (PCA) and Generalized Linear Model (GLM) yielded RMSEs of 0.19 m and 0.18 m respectively, over shallow water depths less than 2 m. In [48], among other pre-processing steps, authors implemented and compared four different empirical SDB (satellite-derived bathymetry) approaches to derive bathymetry from pre-processed Google Earth Engine Sentinel-2 composites.…”

Section: Image-based Bathymetry Estimation Using Machine Learning Andmentioning

confidence: 99%

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

et al. 2019

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The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.

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Bathymetry Determination from High Resolution Satellite Imagery Using Ensemble Learning Algorithms in Shallow Lakes: Case Study El-Burullus Lake

Cited by 36 publications

References 28 publications

Shallow Water Bathymetry Mapping From Uav Imagery Based on Machine Learning

Shallow Water Bathymetry Mapping From Uav Imagery Based on Machine Learning

Method to design a live coral cover sensitive index for multispectral satellite images

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

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