Rapid wavelet-based bathymetry inversion method for nearshore X-band radars

Chernyshov, Pavel; Vrecica, Teodor; Streßer, Michael; Carrasco, Ruben; Toledo, Yaron

doi:10.1016/j.rse.2020.111688

Cited by 25 publications

(18 citation statements)

References 28 publications

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“…A further method to derive bathymetries of coastal zones is to observe nearshore wave propagation and transformation, both by radar (Borge et al 2004;van Dongeren et al 2008) and video (or high-frequency image) recording devices (Aarninkhof et al 2005;Holman et al 2013). Radar-based depth inversion methods require stationary installed devices and process data in the order of days (Chernyshov et al 2020;Gawehn et al 2020). In contrast, UAV-borne video data uses mobile equipment that can be controlled remotely.…”

Section: Introductionmentioning

confidence: 99%

Structure-from-Motion on shallow reefs and beaches: potential and limitations of consumer-grade drones to reconstruct topography and bathymetry

et al. 2021

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Reconstructing the topography of shallow underwater environments using Structure-from-Motion—Multi View Stereo (SfM-MVS) techniques applied to aerial imagery from Unmanned Aerial Vehicles (UAVs) is challenging, as it involves nonlinear distortions caused by water refraction. This study presents an experiment with aerial photographs collected with a consumer-grade UAV on the shallow-water reef of Fuvahmulah, the Maldives. Under conditions of rising tide, we surveyed the same portion of the reef in ten successive flights. For each flight, we used SfM-MVS to reconstruct the Digital Elevation Model (DEM) of the reef and used the flight at low tide (where the reef is almost entirely dry) to compare the performance of DEM reconstruction under increasing water levels. Our results show that differences with the reference DEM increase with increasing depth, but are substantially larger if no underwater ground control points are taken into account in the processing. Correcting our imagery with algorithms that account for refraction did not improve the overall accuracy of reconstruction. We conclude that reconstructing shallow-water reefs (less than 1 m depth) with consumer-grade UAVs and SfM-MVS is possible, but its precision is limited and strongly correlated with water depth. In our case, the best results are achieved when ground control points were placed underwater and no refraction correction is used.

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Section: Introductionmentioning

confidence: 99%

Structure-from-Motion on shallow reefs and beaches: potential and limitations of consumer-grade drones to reconstruct topography and bathymetry

et al. 2021

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“…The Gabor filter bank constructed in [17] with 4 orientations and 6 wavelengths is used to filter each radar image and produce 24 Gabor feature maps. • Wavelet features: in previous studies, wavelet transform has been applied for wave [22] and bathymetry [23] inversion using X-band marine radar data. Here, 1-D discrete wavelet transform (DWT) is applied to each radar image, which generates four feature maps consisting of four channels (i.e., low-low, low-high, high-low, high-high).…”

Section: B Som-based Rain-contaminated Region Identificationmentioning

confidence: 99%

A Novel Scheme for Extracting Sea Surface Wind Information From Rain-Contaminated X-Band Marine Radar Images

Chen

Huang

Haller

2021

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

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The presence of rain degrades the performance of sea surface parameter estimation using X-band marine radar. In this paper, a novel scheme is proposed to improve wind measurement accuracy from rain-contaminated X-band marine radar data. After extracting texture features from each image pixel, the rain-contaminated regions with blurry wave signatures are first identified using a self organizing map (SOM)-based clustering model. Then, a convolutional neural network (CNN) used for image haze removal, i.e., DehazeNet is introduced and incorporated into the proposed scheme for correcting the influence of rain on radar images. In order to obtain wind direction information, curve fitting is conducted on the average azimuthal intensities of rain-corrected radar images. On the other hand, wind speed is estimated from rain-corrected images by training a support vector regression (SVR)-based model. Experiments conducted using datasets from both shipborne and onshore marine radar show that compared to results obtained from images without rain correction, the proposed method achieves relatively high estimation accuracy by reducing measurement errors significantly.

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“…The significant wave height is determined by using the 1D sea wave spectrum based on the Eq. (25). The detailed comparison between the input and the retrieved parameters using the proposed the SWT algorithm as well as the 3D FFT and the CWT methods is shown in Table 2.…”

Section: A Simulated Radar Images and Analysismentioning

confidence: 99%

“…By using the authentic radar data, the experimental results show that the retrieved wave parameters by utilizing the self-adaptive 2D CWT method agree well with the buoy records. Based on the localized 2D CWT, an improved method for retrieving water depth was proposed for near-shore area from the simulated and collected X-band radar images [25]. The experimental results demonstrate that the proposed method based on the 2D CWT has a good performance compared to the reference value.…”

Section: Introductionmentioning

confidence: 99%

A Method for Retrieving Wave Parameters From Synthetic X-Band Marine Radar Images

Wei

Yan

2020

IEEE Access

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Rapid wavelet-based bathymetry inversion method for nearshore X-band radars

Cited by 25 publications

References 28 publications

Structure-from-Motion on shallow reefs and beaches: potential and limitations of consumer-grade drones to reconstruct topography and bathymetry

Structure-from-Motion on shallow reefs and beaches: potential and limitations of consumer-grade drones to reconstruct topography and bathymetry

A Novel Scheme for Extracting Sea Surface Wind Information From Rain-Contaminated X-Band Marine Radar Images

A Method for Retrieving Wave Parameters From Synthetic X-Band Marine Radar Images

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