DocumentCode :
1254418
Title :
Model-based neural network for target detection in SAR images
Author :
Perlovsky, Leonid I. ; Schoendorf, William H. ; Burdick, Bernard J. ; Tye, David M.
Author_Institution :
Nichols Res. Corp., Lexington, MA, USA
Volume :
6
Issue :
1
fYear :
1997
fDate :
1/1/1997 12:00:00 AM
Firstpage :
203
Lastpage :
216
Abstract :
A controversial issue in the research of mathematics of intelligence has been that of the roles of a priori knowledge versus adaptive learning. After discussing mathematical difficulties of combining a priority with adaptivity encountered in the past, we introduce a concept of a model-based neural network, whose adaptive learning is based on a priori models. Applications to target detection in SAR images are discussed. We briefly overview the SAR principles, derive relatively simple physics-based models of SAR signals, and describe model-based neural networks that utilize these models. A number of real-world application examples are presented
Keywords :
adaptive signal processing; learning (artificial intelligence); neural nets; radar detection; radar imaging; synthetic aperture radar; SAR images; a priori knowledge; adaptive learning; model-based neural network; physics-based models; real-world application; target detection; Adaptive systems; Artificial intelligence; Artificial neural networks; Biological neural networks; Intelligent networks; Learning; Mathematical model; Neural networks; Object detection; Target recognition;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/83.552107
Filename :
552107
Link To Document :
بازگشت