Unsupervised neural network classifier for automatic aerial image recognition

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

This article describes the application of the Adaptive Resonance Theory (ART 2-A) network to the problem of Automatic Aerial Image Recognition (AAIR). The classification of aerial images independently of their position and orientation is required for automatic tracking and target recognition. Invariance is achieved by using different invariant feature spaces in combination with unsupervised neural network. The performance of the neural network based classifier in conjunction with several types of invariant AAIR global features, such as the Fourier transform (FT) space, Zernike moments, central moments and polar transforms, are examined. The advantages of this approach are discussed. The ART 2-A distinguished itself with its speed and low number of training vectors. Although a large image data base would be necessary before this approach could be fully validated, the initial results are very promising.

Original languageEnglish
Pages212-215
Number of pages4
StatePublished - 1 Dec 1996
EventProceedings of the 1996 19th Convention of Electrical and Electronics Engineers in Israel - Jerusalem, Isr
Duration: 5 Nov 19966 Nov 1996

Conference

ConferenceProceedings of the 1996 19th Convention of Electrical and Electronics Engineers in Israel
CityJerusalem, Isr
Period5/11/966/11/96

ASJC Scopus subject areas

  • Engineering (all)

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