Remote sensing for shallow bathymetry: A systematic review

He, Jinchen; Zhang, Shuhang; Cui, Xiaodong; Feng, Wei

doi:10.1016/j.earscirev.2024.104957

Earth-Science Reviews

2024

DOI: 10.1016/j.earscirev.2024.104957

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Remote sensing for shallow bathymetry: A systematic review

Jinchen He,

Shuhang Zhang,

Xiaodong Cui

et al.

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2024

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Cited by 2 publications

References 176 publications

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Estimating Water Depth of Different Waterbodies Using Deep Learning Super Resolution from HJ-2 Satellite Hyperspectral Images

Zhang,

Hu,

Wang

et al. 2024

Remote Sensing

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Hyperspectral remote sensing images offer a unique opportunity to quickly monitor water depth, but how to utilize the enriched spectral information and improve its spatial resolution remains a challenge. We proposed a water depth estimation framework to improve spatial resolution using deep learning and four inversion methods and verified the effectiveness of different super resolution and inversion methods in three waterbodies based on HJ-2 hyperspectral images. Results indicated that it was feasible to use HJ-2 hyperspectral images with a higher spatial resolution via super resolution methods to estimate water depth. Deep learning improves the spatial resolution of hyperspectral images from 48 m to 24 m and shows less information loss with peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and spectral angle mapper (SAM) values of approximately 37, 0.92, and 2.42, respectively. Among four inversion methods, the multilayer perceptron demonstrates superior performance for the water reservoir, achieving the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of 1.292 m and 22.188%, respectively. For two rivers, the random forest model proves to be the best model, with an MAE of 0.750 m and an MAPE of 10.806%. The proposed method can be used for water depth estimation of different water bodies and can improve the spatial resolution of water depth mapping, providing refined technical support for water environment management and protection.

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Estimating Water Depth of Different Waterbodies Using Deep Learning Super Resolution from HJ-2 Satellite Hyperspectral Images

Zhang,

Hu,

Wang

et al. 2024

Remote Sensing

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A Two-Stage Nearshore Seafloor ICESat-2 Photon Data Filtering Method Considering the Spatial Relationship

Zuo,

Wang,

Sun

et al. 2024

Remote Sensing

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“Ice, Cloud, and Land Elevation Satellite-2” (ICESat-2) produces photon-point clouds that can be used to obtain nearshore bathymetric data through density-based filtering methods. However, most traditional methods simplified the variable spatial density distribution of a photon to a linear relationship with water depth, causing a limited extraction effect. To address this limitation, we propose a two-stage filtering method that considers spatial relationships. Stage one constructs the adaptive photon density threshold by mapping a nonlinear relationship between the water depth and photon density to obtain initial signal photons. Stage two adopts a seed-point expanding method to fill gaps in initial signal photons to obtain continuous signal photons that more fully reflect seabed topography. The proposed method is applied to ICESat-2 data from Oahu Island and compared with three other density-based filtering methods: AVEBM (Adaptive Variable Ellipse filtering Bathymetric Method), Bimodal Gaussian fitting, and Quadtree Isolation. Our method (F-measure, F = 0.803) outperforms other methods (F = 0.745, 0.598, and 0.454, respectively). The accuracy of bathymetric data gained from seabed photons filtered using our method can achieve 0.615 m (Mean Absolute Error) and 0.716 m (Root Mean Squared Error). We demonstrate the effectiveness of incorporating photon spatial relationships to enhance the filtering of seabed signal photons.

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Remote sensing for shallow bathymetry: A systematic review

Cited by 2 publications

References 176 publications

Estimating Water Depth of Different Waterbodies Using Deep Learning Super Resolution from HJ-2 Satellite Hyperspectral Images

Estimating Water Depth of Different Waterbodies Using Deep Learning Super Resolution from HJ-2 Satellite Hyperspectral Images

A Two-Stage Nearshore Seafloor ICESat-2 Photon Data Filtering Method Considering the Spatial Relationship

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