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Reconstruction of Backscattering and extinction coefficients in Lidar: a stochastic filtering approach

Bioucas-Dias, J. ; Leitão, J. ; Fonseca, E.

IEEE Trans. on Geoscience and Remote Sensing Vol. 42, Nº 2, pp. 604 - 624, February, 2003.

ISSN (print): 0196-2892
ISSN (online): 0196-2892

Journal Impact Factor: 3,157 (in 2008)

Digital Object Identifier: 10.1109/TGRS.2003.817216

Abstract
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