Features for landcover classification of fully polarimetric SAR data

Jorge V. Geaga

doi:10.1117/12.917226

8 May 2012 Features for landcover classification of fully polarimetric SAR data

Jorge V. Geaga

Proceedings Volume 8361, Radar Sensor Technology XVI; 836108 (2012) https://doi.org/10.1117/12.917226
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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 kS_vvk², kS_hvk², kS_hhk² 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.

Citation Download Citation

Jorge V. Geaga "Features for landcover classification of fully polarimetric SAR data", Proc. SPIE 8361, Radar Sensor Technology XVI, 836108 (8 May 2012); https://doi.org/10.1117/12.917226

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