Self-organizing Neural Networks for Unsupervised Classification of Polarimetric SAR Data on Complex Landscapes

Author

Putignano, C. ; Schiavon, G. ; Solimini, D. ; Trisasongko, B.

Author_Institution

DISP, Univ. Tor Vergata, Rome

fYear

2006

fDate

July 31 2006-Aug. 4 2006

Firstpage

504

Lastpage

506

Abstract

This paper refers to a study on the pixel-by-pixel unsupervised classification of a polarimetric SAR image of a Central Italy landscape. The polarimetric data have been processed by self-organizing neural networks to test their performance in classifying a complex landscape. The discrimination accuracy attained by the self-organizing map method is compared both against that of H/A/alpha-Wishart unsupervised procedure and of a supervised scheme.

Keywords

geophysics computing; image classification; image processing; self-organising feature maps; synthetic aperture radar; unsupervised learning; Central Italy landscape; H/A/alpha-Wishart unsupervised procedure; pixel-by-pixel unsupervised classification; polarimetric SAR image; self-organizing map method; self-organizing neural networks; supervised scheme; Artificial neural networks; Automatic testing; Land surface; Multilayer perceptrons; Neural networks; Pixel; Radar scattering; Remote sensing; Spaceborne radar; Synthetic aperture radar;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on

Conference_Location

Denver, CO

Print_ISBN

0-7803-9510-7

Type

conf

DOI

10.1109/IGARSS.2006.134

Filename

4241281