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Gold – A novel deconvolution algorithm with optimization for waveform LiDAR processing / Tan Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 129 (July 2017) . - pp 131 - 150 Titre : Gold – A novel deconvolution algorithm with optimization for waveform LiDAR processing Type de document : Article/Communication Auteurs : Tan Zhou, Auteur ; Sorin C. Popescu, Auteur ; Keith Krause, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 131 - 150 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] déconvolution [Termes IGN] forme d'onde [Termes IGN] optimisation (mathématiques) [Termes IGN] traitement du signal Résumé : (Auteur) Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: (1) direct decomposition, (2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms – the Richardson-Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from the corresponding reference data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference ( Numéro de notice : A2017-349 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.04.021 En ligne : https://dx.doi.org/10.1016/j.isprsjprs.2017.04.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85622 [article]

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