Classification of aerospace targets using superresolution ISAR images

Author

Botha, E.C.

Author_Institution

Dept. of Electr. & Electron. Eng., Pretoria Univ., South Africa

fYear

1994

fDate

34611

Firstpage

138

Lastpage

145

Abstract

This paper describes investigations into a recognition system that takes the 2-D ISAR (inverse synthetic aperture radar) image of an aerospace target as input and classifies the target based on features calculated from the image. Four types of features were implemented, namely geometrical moments, invariant features based on moments, shape features, and quantized energy strips. Nearest-neighbour and neural-net classifiers are considered

Keywords

aircraft; image classification; image resolution; neural nets; radar applications; radar imaging; radar target recognition; synthetic aperture radar; 2-D ISAR image; aerospace targets classification; aircraft; geometrical moments; image features; invariant features; inverse synthetic aperture radar; nearest-neighbour classifiers; neural-net classifiers; quantized energy strips; radar target recognition system; shape features; superresolution ISAR images; Aerospace engineering; Africa; Aircraft; Frequency measurement; Image resolution; Multiple signal classification; Neural networks; Signal resolution; Strips; Target recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Signal Processing, 1994. COMSIG-94., Proceedings of the 1994 IEEE South African Symposium on

Conference_Location

Stellenbosch

Print_ISBN

0-7803-1998-2

Type

conf

DOI

10.1109/COMSIG.1994.512452

Filename

512452