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Proceedings Paper

Application of the H/A/alpha polarimetric decomposition theorem for land classification
Author(s): Eric Pottier; Shane R. Cloude
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Paper Abstract

Classification of Earth terrain components within a full polarimetric SAR image is one of the most important applications of Radar Polarimetry in Remote Sensing. Unsupervised classification procedure, based around neural networks with competitive architecture, is applied to the full polarimetric SAR images of San Francisco Bay from the NASA/JPL AIRSAR data base (1988) for segmentation and clustering of different Earth terrain components. The linear feature vector used during the classification procedure is defined from a new scheme for parameterizing polarimetric scattering problems, which has application in the quantitative analysis of polarimetric SAR data. The method relies on an eigenvalue analysis of the coherency matrix and employs a 3-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data.

Paper Details

Date Published: 23 December 1997
PDF: 12 pages
Proc. SPIE 3120, Wideband Interferometric Sensing and Imaging Polarimetry, (23 December 1997); doi: 10.1117/12.278958
Show Author Affiliations
Eric Pottier, IRESTE (France)
Shane R. Cloude, Applied Electromagnetics (United Kingdom)


Published in SPIE Proceedings Vol. 3120:
Wideband Interferometric Sensing and Imaging Polarimetry
Harold Mott; Wolfgang-Martin Boerner, Editor(s)

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