Computer Science – Learning
Scientific paper
May 1994
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1994jgr....9910847h&link_type=abstract
Journal of Geophysical Research, vol. 99, no. E5, p. 10,847-10,865
Computer Science
Learning
35
Asteroids, Neural Nets, Spectrum Analysis, Classifications, Infrared Spectra, Near Infrared Radiation, Olivine, Pyroxenes, Ultraviolet Spectra, Visible Spectrum
Scientific paper
The 52-color asteroid survey (Bell et al., 1988) together with the 8-color asteroid survey (Zellner et al., 1985) provide a data set of asteroid spectra spanning 0.3-2.5 micrometers. An artificial neural network clusters these asteroid spectra based on their similarity to each other. We have also trained the neural network with a categorization learning output layer in a supervised mode to associate the established clusters with taxonomic classes. Results of our classification agree with Tholen's classification based on the 8-color data alone. When extending the spectral range using the 52-color survey data, we find that some modification of the Tholen classes is indicated to produce a cleaner, self-consistent set of taxonomic classes. After supervised training using our modified classes, the network correctly classifies both the training examples, and additional spectra into the correct class with an average of 90% accuracy. Our classification supports the separation of the K class from the S class, as suggested by Bell et al. (1987), based on the near-infrared spectrum. We define two end-member subclasses which seem to have compositional significance within the S class: the So class, which is olivine-rich and red, and the Sp class, which is pyroxene-rich and less red. The remaining S-class asteroids have intermediate compositions of both olivine and pyroxene and moderately red continua. The network clustering suggests some additional structure within the E-, M-, and P-class asteroids, even in the absence of albedo information, which is the only discriminant between these in the Tholen classification. New relationships are seen between the C class and related G, B, and F classes. However, in both cases, the number of spectra is too small to interpret or determine the significance of these separations.
Howell Ellen S.
Lebofsky Larry A.
Merényi Erzsébet
No associations
LandOfFree
Classification of asteroid spectra using a neural network does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Classification of asteroid spectra using a neural network, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Classification of asteroid spectra using a neural network will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-887596