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Reconstruction of backscatter and extinction coefficients in Lidar: a stochastic filtering approach / José M. Bioucas-Dias in IEEE Transactions on geoscience and remote sensing, vol 42 n° 2 (February 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 2 (February 2004) . - pp 443 - 456 Titre : Reconstruction of backscatter and extinction coefficients in Lidar: a stochastic filtering approach Type de document : Article/Communication Auteurs : José M. Bioucas-Dias, Auteur ; J.M.N. Leitao, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 443 - 456 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] champ aléatoire de Markov [Termes IGN] coefficient de rétrodiffusion [Termes IGN] données lidar [Termes IGN] filtrage statistique [Termes IGN] filtre de Kalman [Termes IGN] processus stochastique Résumé : (Auteur) Reconstruction of the backscatter and extinction coefficients is a crucial step in many quantitative remote sensing applications, such as radar, light detection and ranging (lidar), and sonar. We present a novel stochastic filtering approach for the estimation of the backscatter and extinction coefficients from time-range elastic-backscatter lidar data. The Bayesian perspective is adopted; we take as prior a causal first-order autoregressive Gauss-Markov random field tailored to enforce smoothness on time and range dimensions. By using a reduced-order state-space representation of the prior, we derive a suboptimal stochastic filter that recursively computes the backscatter and extinction coefficients at each range-time inversion cell. The estimator is a kind of adaptive extended Kalman filter, being efficient from the computational point of view. A set of experiments illustrates the effectiveness of the proposed approach, namely its advantage over the classical Klett deterministic approach. Numéro de notice : A2004-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817216 En ligne : https://doi.org/10.1109/TGRS.2003.817216 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26666 [article]

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