Volume Scattering Modeling in PolSAR Decompositions: Study of ALOS PALSAR Data Over Boreal Forest

Model-based approaches for decomposing polarimetric backscatter data from boreal forest are discussed in this paper. Several model-based decompositions are analyzed with respect for the most accurate estimation of the volume scattering component. A novel generalized model for description of the volu...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2011-10, Vol.49 (10), p.3838-3848
Main Authors: Antropov, O., Rauste, Y., Hame, T.
Format: Article
Language:English
Subjects:
Analytical models
Applied geophysics
Atmospheric modeling
Backscatter
Boreal forest
Covariance matrix
Data models
Earth sciences
Earth, ocean, space
Exact sciences and technology
Internal geophysics
model-based decomposition
polarimetry
Scattering
scattering mechanisms
Solid modeling
synthetic aperture radar (SAR)
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Summary:Model-based approaches for decomposing polarimetric backscatter data from boreal forest are discussed in this paper. Several model-based decompositions are analyzed with respect for the most accurate estimation of the volume scattering component. A novel generalized model for description of the volume contribution is proposed when observed backscatter from forest indicates that media does not follow azimuthal symmetry case. The model can be adjusted to the polarimetric synthetic aperture radar (PolSAR) data itself, taking into consideration higher sensitivity of HH against VV backscattering term to the presence of canopy at L-band. The model is general enough to allow a broad range of canopies to be modeled and is shown to comply with several earlier proposed volume scattering mechanism models. It is afterward incorporated in the Freeman-Durden three-component decomposition, yielding an improved modification. The performance of the proposed modification is evaluated using multitemporal ALOS PALSAR data acquired over Kuortane area in central Finland, representing typical mixed boreal forestland. Several decompositions are also benchmarked in order to see how they satisfy physical requirements when decomposing covariance matrix into a weighted sum of individual scattering mechanism contributions. When using experimental data, the proposed decomposition is shown to better satisfy non-negativity constraints for the covariance matrix eigenvalues at each decomposition step with less additional PolSAR data averaging needed. Discussed decompositions are also evaluated for the accuracy of initial stratification based on dominating scattering mechanism using ground reference data.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2011.2138146