Parametric analysis of achievable signal-to-noise ratio of a full-waveform space-based backscatter lidar

Tcherniavski, Iouri; Dudelzak, Alexander E.; Koujelev, Alexander

doi:10.1080/01431161.2011.568532

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…19 Therefore, the quality of full-waveform LiDAR data is mainly affected by factors in the following four aspects. 19 Therefore, the quality of full-waveform LiDAR data is mainly affected by factors in the following four aspects.…”

Section: Introductionmentioning

confidence: 99%

Signal-to-noise ratio–based quality assessment method for ICESat/GLAS waveform data

Nie

Wang

et al. 2014

Opt. Eng

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Data quality determines the accuracy of results associated with remote sensing data processing and applications. However, few effective studies have been carried out on quality assessment methods for the fullwaveform light detecting and ranging data. Using the geoscience laser altimeter system (GLAS) waveform data as an example, a signal-to-noise ratio (SNR)-based waveform quality assessment method is proposed to analyze the relationship between the SNR and its controlling factors, i.e., laser type, laser using time, topographic relief, and land cover type, and study the impacts of these factors on the quality of the GLAS waveform data. Results show that the SNR-based data quality assessment method can quantitatively and effectively assess the GLAS waveform data quality. The SNR linearly attenuates with the laser using time, and the attenuation rate varies with laser type. The topographic relief is inversely correlated with the SNR of the GLAS data. As the land cover structure (especially the vertical structure) becomes more complex, the SNR of the GLAS data decreases. It was found that land cover types in descending order of the SNR values are desert, farmland, water body, grassland, city, and forest.

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Section: Introductionmentioning

confidence: 99%