Depth-sounding lidar: performance and models

Steinvall, Ove; Koppari, Kurt R.; Lejdebrink, Ulf; Winell, Johan; Nilsson, Magnus; Ellsen, Rutger; Gjellan, Einar

doi:10.1117/12.243551

Cited by 10 publications

(2 citation statements)

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“…The performance for detection of small targets and sea bottom features was studied e.g. by Guenther et al, 1 West and Lillycrop, 2 and Steinvall et al 3,4 The work by Tuell et al 5,6 have demonstrated the potential to produce estimates of green laser reflectance by analyzing lidar waveforms, which provides information for the fusion of lidar and hyperspectral data for classification of e.g. sea floor vegetation.…”

Section: Introductionmentioning

confidence: 98%

Processing of airborne lidar bathymetry data for detailed sea floor mapping

Tulldahl

2014

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; And Military Applications in Hyperspectral Imagin

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Airborne bathymetric lidar has proven to be a valuable sensor for rapid and accurate sounding of shallow water areas. With advanced processing of the lidar data, detailed mapping of the sea floor with various objects and vegetation is possible. This mapping capability has a wide range of applications including detection of mine-like objects, mapping marine natural resources, and fish spawning areas, as well as supporting the fulfillment of national and international environmental monitoring directives. Although data sets collected by subsea systems give a high degree of credibility they can benefit from a combination with lidar for surveying and monitoring larger areas. With lidar-based sea floor maps containing information of substrate and attached vegetation, the field investigations become more efficient. Field data collection can be directed into selected areas and even focused to identification of specific targets detected in the lidar map. The purpose of this work is to describe the performance for detection and classification of sea floor objects and vegetation, for the lidar seeing through the water column. With both experimental and simulated data we examine the lidar signal characteristics depending on bottom depth, substrate type, and vegetation. The experimental evaluation is based on lidar data from field documented sites, where field data were taken from underwater video recordings. To be able to accurately extract the information from the received lidar signal, it is necessary to account for the air-water interface and the water medium. The information content is hidden in the lidar depth data, also referred to as point data, and also in the shape of the received lidar waveform. The returned lidar signal is affected by environmental factors such as bottom depth and water turbidity, as well as lidar system factors such as laser beam footprint size and sounding density.

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Section: Introductionmentioning

confidence: 98%

Processing of airborne lidar bathymetry data for detailed sea floor mapping

Tulldahl

2014

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; And Military Applications in Hyperspectral Imagin

View full text Add to dashboard Cite

show abstract

“…The performance for detection of small targets and sea bottom features was studied e.g. by Guenther et al, 7 West and Lillycrop, 8 and Steinvall et al 9,10 Recent work by Tuell et al 11,12 have demonstrated the potential to produce estimates of green laser reflectance and optical properties of the water column by analyzing lidar waveforms, which provides for the fusion of lidar and hyperspectral data for classification of e.g. sea floor vegetation.…”

Section: Introductionmentioning

confidence: 98%

Sea floor classification from airborne lidar data

et al. 2007

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Airborne depth sounding lidar has proven to be a valuable sensor for rapid and accurate sounding of shallow areas. The received lidar pulse echo contains information of the sea floor depth, but also other data can be extracted. We currently perform work on bottom classification and water turbidity estimation based on lidar data. In this paper we present the theoretical background and experimental results on bottom classification. The algorithms are developed from simulations and then tested on experimental data from the operational airborne lidar system Hawk Eye II. We compare the results to field data taken from underwater video recordings. Our results indicate that bottom classification from airborne lidar data can be made with high accuracy.

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Classification of aquatic macrovegetation and substrates with airborne lidar

Tulldahl

Wikström

2012

Remote Sensing of Environment

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Depth-sounding lidar: performance and models

Cited by 10 publications

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Processing of airborne lidar bathymetry data for detailed sea floor mapping

Processing of airborne lidar bathymetry data for detailed sea floor mapping

Sea floor classification from airborne lidar data

Classification of aquatic macrovegetation and substrates with airborne lidar

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