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USING BISPECTRAL FULL-WAVEFORM LIDAR TO MAP SEAMLESS COASTAL HABITATS IN 3D

Abstract : Mapping coastal habitats is essential to their preservation, but the presence of water hinders seamless data collection over land-water interfaces. Thanks to its dual-wavelength and optical properties, topo-bathymetric lidar can address this task efficiently. Topo-bathymetric lidar waveforms contain relevant information to classify land and water covers automatically but are rarely analysed for both infrared and green wavelengths. The present study introduces a point-based approach for the classification of coastal habitats using bispectral waveforms of topo-bathymetric lidar surveys and machine learning. Spectral features and differential elevations are fed to a random forest algorithm to produce three-dimensional classified point clouds of 17 land and sea covers. The resulting map reaches an overall accuracy of 86%, and 65% of the prediction probabilities are above 0.60. Using this prediction confidence, it is possible to map coastal habitats and eliminate the classification errors due to noise in the data, that generate a clear tendency of the algorithm to overestimate some classes at the expense of some others. By filtering out points with a low prediction confidence (under 0.7), the classification can be highly improved and reach an overall accuracy of 97%.
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https://hal-insu.archives-ouvertes.fr/insu-03691672
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Submitted on : Thursday, June 9, 2022 - 11:36:02 AM
Last modification on : Friday, June 24, 2022 - 4:07:45 AM

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Mathilde Letard, A. Collin, Dimitri Lague, Thomas Corpetti, y. Pastol, et al.. USING BISPECTRAL FULL-WAVEFORM LIDAR TO MAP SEAMLESS COASTAL HABITATS IN 3D. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH (Copernicus Publications), 2022, XLIII-B3-2022, pp.463 - 470. ⟨10.5194/isprs-archives-xliii-b3-2022-463-2022⟩. ⟨insu-03691672⟩

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