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Proceedings Paper

Features for landcover classification of fully polarimetric SAR data
Author(s): Jorge V. Geaga
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Paper Abstract

We have previously shown that Stokes eigenvectors can be numerically extracted from the Kennaugh(Stokes) matrices of both single-look and multilook fully polarimetric SIR-C data. The extracted orientation and ellipticity parameters of the Stokes eigenvector were found to be related to the Huynen orientation and helicity parameters for single-look fully polarimetric SIR-C data. We formally show in this paper that these two parameters, which diagonalize the Sinclair matrices of the single-look data, belong to a set of parameters which diagonalize the Kennaugh matrices of single-look data. Along with the cross sections kSvvk2, kShvk2, kShhk2 and the Span, the eigenvalues of the Kennaugh matrix and the covariance matrix are used as input features in the development of a neural net landcover classifier for SIR-C data.

Paper Details

Date Published: 9 May 2012
PDF: 14 pages
Proc. SPIE 8361, Radar Sensor Technology XVI, 836108 (9 May 2012); doi: 10.1117/12.917226
Show Author Affiliations
Jorge V. Geaga, Consultant (United States)


Published in SPIE Proceedings Vol. 8361:
Radar Sensor Technology XVI
Kenneth I. Ranney; Armin W. Doerry, Editor(s)

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