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Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms / Gustavo H.X. Shiroma in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)
Titre : Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms Type de document : Article/Communication Auteurs : Gustavo H.X. Shiroma, Auteur ; Marco Lavalle, Auteur Année de publication : 2020 Article en page(s) : pp 754 - 3777 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bande L
[Termes IGN] décomposition de Gauss
[Termes IGN] Gabon
[Termes IGN] histogramme
[Termes IGN] image captée par drone
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation 3D
[Termes IGN] polarimétrie radar
[Termes IGN] rétrodiffusion
[Termes IGN] structure de la végétationRésumé : (auteur) This article demonstrates how 3-D vegetation structure can be approximated by interferometric synthetic aperture radar (InSAR) backscatter-height histograms. Single-look backscatter measurements are plotted against the InSAR phase height and are aggregated spatially over a forest patch to form a 3-D histogram, referred to as InSAR backscatter-height histogram or simply InSAR histogram. InSAR histograms resemble LiDAR waveforms, suggesting that existing algorithms used to retrieve canopy height and ground topography from radar tomograms or LiDAR waveforms can be applied to InSAR histograms. Three algorithms are evaluated to generate maps of digital terrain, surface, and canopy height models: Gaussian decomposition, quantile, and backscatter threshold. Full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data collected over the Gabonese Lopé National Park during the 2016 AfriSAR campaign are used to illustrate and compare the performance of the algorithms for the HH, HV, VV, HH+VV, and HH−VV polarimetric channels. Results show that radar-derived maps using the InSAR histograms differ by 4 m (top-canopy), 5 m (terrain), and 6 m (forest height) in terms of average root-mean-square errors (RMSEs) from standard maps derived from full-waveform laser, vegetation, and ice sensor (LVIS) LiDAR measurements. Numéro de notice : A2020-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2956989 Date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2956989 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95099
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 754 - 3777[article]
Titre : A new waveform decomposition method for multispectral LiDAR Type de document : Article/Communication Auteurs : Shalei Song, Auteur ; Binhui Wang, Auteur ; Wei Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 40 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] couvert végétal
[Termes IGN] décomposition de Gauss
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forme d'onde pleine
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Information derived from waveform decomposition of full-waveform light detection and ranging (LiDAR) data has been widely used in vegetation detection and three-dimensional urban terrain modeling to investigate and interpret the structural diversity of surface coverage. Most prevailing waveform decomposition methods involve only a single wavelength, but these methods do not apply to full-waveform multispectral LiDAR (FWMSL) systems that simultaneously acquire spectral and geometric information. In this paper, we propose a new multispectral waveform decomposition (MSWD) method in order to explore the potential advantages of the FWMSL system. Both simulated data and measured data from our FWMSL system were used to evaluate the performance of the proposed method. The coefficient of determination (R2), root mean square error (RMSE), and relative error (rRMSE) metrics suggest that the decomposition results derived from MSWD exhibit a comparable overall fitting accuracy as a single wavelength waveform decomposition (SWWD) method. We also propose a new evaluation indicator, relative neighbor distance error (RNDE), to represent the relative error in the distance between adjacent targets. The simulation results present clear superiority of MSWD over SWWD in terms of discovering weak or overlapping components and retrieving accurate waveform parameters. The experimental results demonstrated a considerable improvement in RNDE (0.0100–0.0610) over the prevailing SWWD method (0.0566–0.2833). Unlike SWWD, MSWD initializes waveform components using mutually complementary wavelengths thus delivering higher completeness and accuracy. MSWD can be extended to other FWMSL or full-waveform hyperspectral LiDAR systems with additional wavelengths. Numéro de notice : A2019-127 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.014 Date de publication en ligne : 22/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92438
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 40 - 49[article]Réservation
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Titre : DEM refinement by low vegetation removal based on the combination of full waveform data and progressive TIN densification Type de document : Article/Communication Auteurs : Hongchao Ma, Auteur ; Weiwei Zhou, Auteur ; Liang Zhang, Auteur Année de publication : 2018 Article en page(s) : pp 260 - 271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de Levenberg-Marquardt
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] contour
[Termes IGN] décomposition de Gauss
[Termes IGN] densification
[Termes IGN] extraction de la végétation
[Termes IGN] filtrage de la végétation
[Termes IGN] forme d'onde pleine
[Termes IGN] hauteur de la végétation
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de points
[Termes IGN] signal laser
[Termes IGN] Triangulated Irregular NetworkRésumé : (Auteur) Filtering of low vegetation with height less than approximately 1.5 m is a challenging problem, especially in mountainous areas covered by heavy low foliage, bushes and sub-shrubberies, etc. The paper proposes an approach for obtaining a more accurate Digital Elevation Model (DEM) by removing low vegetation from point cloud. The approach combines point cloud with full waveform data, and begins by filtering point cloud by way of progressive TIN densification (PTD) method. Ground points are thus extracted, but mixed with false ground points, which are mainly from low vegetation and other manmade low objects. Gaussian decomposition by grouping Levenberg–Marquardt (LM) algorithm with F test is performed for the full waveforms corresponding to the extracted ground points. Echo widths and backscattering coefficients are calculated based on the parameters extracted from the decomposition, and used to discriminate points of low vegetation from points of other low objects, allowing the false ground points reflected from low vegetation to be labeled. New elevation values are calculated from the last echoes of the waveforms from low vegetation, and the DEM is updated by replacing the original elevations with the calculated ones. The resultants are assessed both quantitatively by check points and qualitatively by rendered DEM and contour lines generated from it. The accuracy of the refined DEM with low vegetation removal increases by 31% compared with the original DEM in the experiment, showing the effectiveness of the proposed approach. Numéro de notice : A2018-539 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.009 Date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91553
in ISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 260 - 271[article]Réservation
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Titre : Decomposition of LiDAR waveforms by B-spline-based modeling Type de document : Article/Communication Auteurs : Xiang Shen, Auteur ; Qing-Quan Li, Auteur ; Guofeng Wu, Auteur ; Jiasong Zhu, Auteur Année de publication : 2017 Article en page(s) : pp 182 - 191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] B-Spline
[Termes IGN] décomposition de Gauss
[Termes IGN] distribution, loi de
[Termes IGN] forme d'onde pleine
[Termes IGN] traitement du signal
[Termes IGN] transformation géométrique
[Termes IGN] translationRésumé : (Auteur) Waveform decomposition is a widely used technique for extracting echoes from full-waveform LiDAR data. Most previous studies recommended the Gaussian decomposition approach, which employs the Gaussian function in laser pulse modeling. As the Gaussian-shape assumption is not always satisfied for real LiDAR waveforms, some other probability distributions (e.g., the lognormal distribution, the generalized normal distribution, and the Burr distribution) have also been introduced by researchers to fit sharply-peaked and/or heavy-tailed pulses. However, these models cannot be universally used, because they are only suitable for processing the LiDAR waveforms in particular shapes. In this paper, we present a new waveform decomposition algorithm based on the B-spline modeling technique. LiDAR waveforms are not assumed to have a priori shapes but rather are modeled by B-splines, and the shape of a received waveform is treated as the mixture of finite transmitted pulses after translation and scaling transformation. The performance of the new model was tested using two full-waveform data sets acquired by a Riegl LMS-Q680i laser scanner and an Optech Aquarius laser bathymeter, comparing with three classical waveform decomposition approaches: the Gaussian, generalized normal, and lognormal distribution-based models. The experimental results show that the B-spline model performed the best in terms of waveform fitting accuracy, while the generalized normal model yielded the worst performance in the two test data sets. Riegl waveforms have nearly Gaussian pulse shapes and were well fitted by the Gaussian mixture model, while the B-spline-based modeling algorithm produced a slightly better result by further reducing 6.4% of fitting residuals, largely benefiting from alleviating the adverse impact of the ringing effect. The pulse shapes of Optech waveforms, on the other hand, are noticeably right-skewed. The Gaussian modeling results deviated significantly from original signals, and the extracted echo parameters were clearly inaccurate and unreliable. The B-spline-based method performed significantly better than the Gaussian and lognormal models by reducing 45.5% and 11.5% of their fitting errors, respectively. Much more precise echo properties can accordingly be retrieved with a high probability. Benefiting from the flexibility of B-splines on fitting arbitrary curves, the new method has the potentiality for accurately modeling various full-waveform LiDAR data, whether they are nearly Gaussian or non-Gaussian in shape. Numéro de notice : A2017-334 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.03.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.03.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85487
in ISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 182 - 191[article]Réservation
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Titre : Stepwise decomposition of full-waveform data based on Levenberg Marquardt Type de document : Article/Communication Auteurs : Pengcheng Li, Auteur ; Qing Xu, Auteur ; Pingyuan Cui, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 14 - 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de Levenberg-Marquardt
[Termes IGN] compensation par moindres carrés
[Termes IGN] décomposition de Gauss
[Termes IGN] données lidar
[Termes IGN] données localisées 3DRésumé : (auteur) Compared with traditional airborne LiDAR, the advantage of full-waveform LiDAR is that it digitalizes the full backscattered information. Waveform decomposition is the most important part of waveform data processing. A method of stepwise decomposition of full-waveform data based on Levenberg Marquardt has been proposed, which employs stepwise decomposition and uses Levenberg Marquardt, which is a Gaussian function with non-linear least squares fitting algorithm, to obtain precise fitting waveform. Experiment results with 4 different waveform data acquired by RIEGL airborne LiDAR have proved that this method is available and effective. Numéro de notice : A2014-778 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78788
in Studies in Surveying and Mapping Science > vol 2 (2014) . - pp 14 - 19[article]Documents numériques
en open access
Stepwise decomposition of full-waveform dataAdobe Acrobat PDF
