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Confidence Measure of the Shallow-Water Bathymetry Map Obtained through the Fusion of Lidar and Multiband Image Data
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2021
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Source: Journal of Remote Sensing, 2021
[PDF-2.52 MB]
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Journal Title:Journal of Remote Sensing
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Description:With the advancement of Lidar technology, bottom depth ( H ) of optically shallow waters (OSW) can be measured accurately with an airborne or space-borne Lidar system ( H Lidar hereafter), but this data product consists of a line format, rather than the desired charts or maps, particularly when the Lidar system is on a satellite. Meanwhile, radiometric measurements from multiband imagers can also be used to infer H ( H imager hereafter) of OSW with variable accuracy, though a map of bottom depth can be obtained. It is logical and advantageous to use the two data sources from collocated measurements to generate a more accurate bathymetry map of OSW, where usually image-specific empirical algorithms are developed and applied. Here, after an overview of both the empirical and semianalytical algorithms for the estimation of H from multiband imagers, we emphasize that the uncertainty of H imager varies spatially, although it is straightforward to draw regressions between H Lidar and radiometric data for the generation of H imager . Further, we present a prototype system to map the confidence of H imager pixel-wise, which has been lacking until today in the practices of passive remote sensing of bathymetry. We advocate the generation of a confidence measure in parallel with H imager , which is important and urgent for broad user communities.
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Source:Journal of Remote Sensing, 2021
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ISSN:2694-1589
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Rights Information:CC BY
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