Title :
The Impact of Model Based Despeckling on Soil Moisture Estimation
Author :
Dusan Gleich;Peter Planinsic;Matej Kseneman;Zarko Cucej
Author_Institution :
Fac. of Electr. Eng. & Comput. Sci., Univ. of Maribor, Maribor, Slovenia
Abstract :
This paper presents model based despeckling and soil moisture estimation using TerraSAR-X data. The impact of despeckling on soil moisture estimation is presented and compared with real-ground measurements. This paper presents the model based despeckling using a maximum a posteriori approach. The prior is modeled using the auto-binomial model and Gauss Markov random field (GMRF). Both models belong to the family of Gibbs-Random fields. The likelihood is in both methods presented with the Gaussian pdf. The texture parameters of the ABM and GMRF models are estimated using the evidence maximization approach.
Keywords :
"Soil moisture","Bayesian methods","Speckle","Data mining","Gaussian processes","Computer science","Remote sensing","Gaussian distribution","Pixel","Moisture measurement"
Conference_Titel :
Systems, Signals and Image Processing, 2009. IWSSIP 2009. 16th International Conference on
Print_ISBN :
978-1-4244-4530-1
DOI :
10.1109/IWSSIP.2009.5367759