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Proceedings Paper

Comparison of algorithms for the classification of polarimetric SAR data
Author(s): V. Alberga; D. Borghys; G. Satalino; D. K. Staykova; A. Borghgraef; F. Lapierre; C. Perneel
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Paper Abstract

Most of the current SAR systems aquire fully polarimetric data where the obtained scattering information can be represented by various coherent and incoherent parameters. In previous contributions we reviewed these parameters in terms of their "utility" for landcover classification, here, we investigate their impact on several classification algoritms. Three classifiers: the minimum-distance classifier, a multi-layer perceptron (MLP) and one based on logistic regression (LR) were applied on an L-Band scene acquired by the E-SAR sensor. MLP and LR were chosen because they are robust w.r.t. the data statistics. An interesting result is that MLP gives better results on the coherent parameters while LR gives better results on the incoherent parameters.

Paper Details

Date Published: 29 September 2009
PDF: 11 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74771V (29 September 2009); doi: 10.1117/12.829756
Show Author Affiliations
V. Alberga, Royal Belgian Military Academy (Belgium)
D. Borghys, Royal Belgian Military Academy (Belgium)
G. Satalino, ISSIA, CNR (Italy)
D. K. Staykova, Göteborg Univ. (Sweden)
A. Borghgraef, Royal Belgian Military Academy (Belgium)
F. Lapierre, Royal Belgian Military Academy (Belgium)
C. Perneel, Royal Belgian Military Academy (Belgium)

Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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