Self-organizing neural networks for unsupervised classification of polarimetric SAR data on complex landscapes

被引：1
|
作者：
Putignano, C. [1 ]
Schiavon, G. [1 ]
Solimini, D. [1 ]
Trisasongko, B. [2 ]
机构：
[1] Univ Roma Tor Vergata, DISP, Rome, Italy
[2] Bogor Agr Univ, Bogor, Indonesia
来源：
2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8 | 2006年
关键词：
D O I：
10.1109/IGARSS.2006.134
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
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.
引用
收藏
页码：504 / +
页数：2
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