One-dimensional signal processing techniques for airborne laser bathymetry

Wong, Hong; Antoniou, A.

doi:10.1109/36.285187

Cited by 22 publications

(9 citation statements)

References 14 publications

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“…The linear water-dependent bias in the fi tting method might be explained by this approximation. A frequency-based method to differentiate the bottom refl ection, presented in Wong and Antoniou (1994), is being tested as an enhancement of the peaks detection after Phase 1 of our fi tting method, even though this method was not developed for very shallow waters. In addition, mathematical formulations of the water backscatter depending on turbidity need to be incorporated into our model.…”

Section: Discussionmentioning

confidence: 99%

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Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near‐infrared and green signals

Allouis

Bailly

Pastol

et al. 2010

Earth Surf Processes Landf

107

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Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non-navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is diffi cult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near-infrared (NIR), Raman] of the SHOALS-1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The fi rst method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classifi cation and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (−4·2 cm verusu −4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (−3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method.

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Section: Discussionmentioning

confidence: 99%

“…The diffi culty lies in preserving the information content at a suffi cient level (Wong and Antoniou, 1994). In addition, our method did not take the water column backscatter into account.…”

Section: Discussionmentioning

confidence: 99%

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near‐infrared and green signals

Allouis

Bailly

Pastol

et al. 2010

Earth Surf Processes Landf

107

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“…The smooth waveform obtained from this technique preserves peak position, pulse width and skew of the laser reflection (Wong and Antoniou, 1994).…”

Section: Methodsmentioning

confidence: 99%

Full Waveform Lidar Exploitation Technique and Its Evaluation in the Mixed Forest Hilly Region

Chhatkuli¹,

Mano²,

Kogure³

et al. 2012

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

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Commission VII, Theme Session VII/7, III/2, V/3 KEY WORDS: LIDAR, Analysis, DEM/DTM, Point Cloud, Accuracy ABSTRACT:In this paper a full waveform exploitation technique and its evaluation in the mixed forest hilly region is presented. The increment in ground penetration by using the full waveform exploitation technique compared to the discrete LiDAR pulses during autumn and winter season is evaluated. The results showed that the technique used for the full waveform exploitation has effectively increased the ground penetration by 50 % and 20 %, respectively, during autumn and winter in the mixed forest hilly region compared to the discrete return pulses. The accuracy test of the LiDAR derived terrain model constructed from the discrete LiDAR pulses and full waveform LiDAR pulses obtained during autumn and winter has also been performed. The RMSE of the LiDAR derived DTM with 1m grid size constructed from the discrete LiDAR pulses obtained during autumn and winter were 0.73 m and 0.22 m respectively. Likewise, the RMSE of the LiDAR derived DTM constructed from the full waveform LiDAR pulses obtained during autumn and winter were 0.59 m and 0.21 m respectively. The results also showed that by using full waveform return pulses, DTM constructed for both seasons were improved compared to the DTM generated from discrete LiDAR pulses.

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“…This has encouraged the development of remote sensing based bathymetric techniques. The airborne LIDAR technique, based on laser telemetry, is still very expensive and available on aerial platform only, so that it covers limited areas (Wong and Antoniou, 1994). Radar imagery is very sensitive to sea surface roughness that is influenced by bottom topography in shallow areas (Alpers and Hennings 1984).…”

Section: Introductionmentioning

confidence: 99%

Comparison of bathymetric estimation using different satellite images in coastal sea waters

Minghelli-Roman

Goreac

Mathieu

et al. 2009

International Journal of Remote Sensing

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Bathymetric estimation can be obtained from multispectral satellite images for shallow waters. The method is based on the rotation of a pair of spectral bands. One of the resulting images is depth-dependent. Therefore several pixels corresponding to different depths are required to numerically evaluate the linear relation between the pixel values and the real depth for a training area. The aim of this study is to compare, for one bathymetric estimation method and one mesotrophic site, the results of depth estimation with a large panel of satellite and aerial images: CASI, QUICKBIRD, CHRIS PROBA, ETM, HYPERION and MeRIS. For each image the pair of spectral bands chosen to compute the bathymetry has been optimized. Error on depth estimation has been computed on two regions of the image: the training area and a validation area. This comparison is discussed to identify the influence of image parameters (spectral bands, S/N ratio, spatial resolution, and quantization) on the bathymetric results and to propose the most adapted image parameters for bathymetric estimation. For validation purposes, we compared the results obtained with a CASI image matching the optimized parameters in an oligotrophic site in the Red Sea.

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One-dimensional signal processing techniques for airborne laser bathymetry

Cited by 22 publications

References 14 publications

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near‐infrared and green signals

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near‐infrared and green signals

Full Waveform Lidar Exploitation Technique and Its Evaluation in the Mixed Forest Hilly Region

Comparison of bathymetric estimation using different satellite images in coastal sea waters

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