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Proceedings Paper

Automated galaxy classification using artificial neural networks
Author(s): Steve C. Odewahn
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Paper Abstract

Current efforts to perform automatic galaxy classification using artificial neural network image classifiers are reviewed. For both digitized photographic Schmidt plate data and newly obtained WFPC2 imagery from the Hubble Space Telescope, a variety of 2D photometric parameter space produce a segregation of Hubble types. Through the use of hidden node layers. a neural network is capable of mapping complicated, highly nonlinear data space. This powerful technique is used to map a multivariate photometric parameter space to the revised Hubble system of galaxy classification.

Paper Details

Date Published: 30 October 1997
PDF: 10 pages
Proc. SPIE 3164, Applications of Digital Image Processing XX, (30 October 1997); doi: 10.1117/12.279549
Show Author Affiliations
Steve C. Odewahn, Calfornia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3164:
Applications of Digital Image Processing XX
Andrew G. Tescher, Editor(s)

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