Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery

Zhang, Xuechun; Ma, Yi; Zhang, Jingyu

doi:10.3390/rs12183027

Cited by 16 publications

(3 citation statements)

References 35 publications

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“…While the authors invested in thorough preprocessing of the image data based on Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and the Cloud Shadow Approach (CSA) for atmospheric corrections, they only apply a simple band ratio model as basis for their colour-to-depth regression. Zhang et al (2020) developed an Inherent Optical Parameters Linear Model (IOPLM) to estimate shallow water bathymetry of a coastal scene based on WorldView-2 and Sentinel-2A multispectral images. Cahalane et al (2019) compare different satellite datasets (Landsat 8, RapidEye, and Pleiades) and use a log ratio transform to derive bathymetry.…”

Section: Related Workmentioning

confidence: 99%

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Mandlburger

Kölle

Nübel

et al. 2021

PFG

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Besides airborne laser bathymetry and multimedia photogrammetry, spectrally derived bathymetry provides a third optical method for deriving water depths. In this paper, we introduce BathyNet, an U-net like convolutional neural network, based on high-resolution, multispectral RGBC (red, green, blue, coastal blue) aerial images. The approach combines photogrammetric and radiometric methods: Preprocessing of the raw aerial images relies on strict ray tracing of the potentially oblique image rays, considering the intrinsic and extrinsic camera parameters. The actual depth estimation exploits the radiometric image content in a deep learning framework. 3D water surface and water bottom models derived from simultaneously captured laser bathymetry point clouds serve as reference and training data for both image preprocessing and actual depth estimation. As such, the approach highlights the benefits of jointly processing data from hybrid active and passive imaging sensors. The RGBC images and laser data of four groundwater supplied lakes around Augsburg, Germany, captured in April 2018 served as the basis for testing and validating the approach. With systematic depth biases less than 15 cm and a standard deviation of around 40 cm, the results satisfy the vertical accuracy limit Bc7 defined by the International Hydrographic Organization. Further improvements are anticipated by extending BathyNet to include a simultaneous semantic segmentation branch.

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Section: Related Workmentioning

confidence: 99%

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Mandlburger

Kölle

Nübel

et al. 2021

PFG

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“…The research areas in previous SAR shallow sea underwater topography detections have mainly been offshore shallow areas. For the remote sensing detection of the island reef topography, the main methods are analysis models, semianalytical semiempirical models and statistical models based on optical remote sensing images [22][23][24]. The detection effect of SAR for the underwater topography of islands and reefs is still unknown.…”

Section: Introductionmentioning

confidence: 99%

Underwater Topography Detection and Analysis of the Qilianyu Islands in the South China Sea Based on GF-3 SAR Images

et al. 2020

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Shallow sea underwater topography plays an important role in the development of islands and reefs. The Qilianyu Islands, located in Xisha, South China Sea, are a key area for the development and utilization of the South China Sea. Compared with traditional underwater topography detection methods, synthetic aperture radar (SAR) has the advantages of low cost, short time consumption, and the large-scale detection of shallow water topography. The GF-3 satellite is the first SAR satellite launched by China, and its ability to probe shallow sea topography has never been assessed. To detect the underwater topography of the Qilianyu Islands and test the application of GF-3 SAR data in shallow sea underwater topography detection, this paper implements the SAR shallow sea underwater topography detection model, the tidal information corresponding to the imaging time of the SAR image, and six GF-3 SAR images to detect the underwater topography of the Qilianyu island and reefs. The detection results have been analyzed from different imaging times, different water depths and different polarization modes, and the first four SAR images show promising detection results. The average absolute error (MAE) and average relative error (MRE) of the results are 1.5 m and 14.33%, respectively, which demonstrates that GF-3 SAR images have an impressive performance in underwater topography detection of South China Sea island reefs.

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“…Yang et al [22] introduced IOPs in turbid waters to retrieve the euphotic zone depth in inland waters using a modifi ed quasi-analytical algorithm (QAA) . Zhang et al [23] propose d a linear model known as the inherent optical parameter linear model (IOPIM) to estimate shallow-water depth using high-spat ial resolution multispectral images. The findings from the IOPI M study demonstrated the potential of using inherent optical par ameters (IOPs) to enhance the accuracy of water depth estimatio n. Huang et al [24] proposed an updated quasi-analytical algorit hm (UQAA) and verified its feasibility .…”

mentioning

confidence: 99%

Shallow-Water Bathymetry Retrieval Based on an Improved Deep Learning Method Using GF-6 Multispectral Imagery in Nanshan Port Waters

Shen,

Chen,

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In a seaport, accurate bathymetric maps are valuabl e for both environmental and economic reasons. One of the main complementary methods for measuring shallow water depth is th e retrieval of the water depth by satellite. The results of the water depth inversion are greatly influenced by factors related to water quality. The proposed Updated Quasi Analysis Algorithm (UQA A) allows for the calculation of water quality factors, and their sp atial distribution characteristic strongly correlates with the trend in water depth distribution. By using satellite-derived bathymetr y, these parameters can be used in the model training to extract t he underwater terrain. This paper proposes the idea of combinin g the UQAA with a Convolutional Neural Network (CNN)-based deep learning framework to retrieve the depth of the water and a utomatically extract the underwater terrain. We compare four di fferent existing machine learning algorithms as baselines, using G F-6 multispectral remote sensing images and in-situ depth data in Nanshan Port as a priori validation set. We find that the result of the CNN model using the UQAA is better than other baselines, w here the root mean square error (RMSE) was down to 0.55 m, the mean relative error (MRE) was 6.63%, and the R 2 was 0.92. The developed method which introduces the water quality factors con taining geographic information as feature quantities, provides a n ew direction for further improvement. (we4237/Batymetry_UQA A_CNN (github.com))

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Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery

Cited by 16 publications

References 35 publications

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Underwater Topography Detection and Analysis of the Qilianyu Islands in the South China Sea Based on GF-3 SAR Images

Shallow-Water Bathymetry Retrieval Based on an Improved Deep Learning Method Using GF-6 Multispectral Imagery in Nanshan Port Waters

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