Using Neural Networks For The Detection, Extraction And Pre-Classification Of Spectra In Objective Prism Images

Abstract

A system based on a multi--layer feed--forward neural network is presented, which is able to detect and extract single, multiple or overlapped spectra from objective prism images, and to perform a coarse classification. The data obtained are currently fed to subsequent wavelength calibration and detailed rule-- based classification steps. Given the encouraging results obtained, we plan to use the neural network approach to perform the immediate classification of the extracted spectra with an accuracy of about 2 spectral sub-classes. key-words -- spectra -- objective prism -- pre-classification -- neural networks 1. INTRODUCTION One of the most difficult tasks to be accomplished in astronomy is identifying and classifying objects. Several attempts have been made in the various astronomical fields of interest to perform these tasks automatically , but it was often noted that the human eye performs better than mathematical algorithms for this purpose. This consideration led us to test ..