Automated star/galaxy discrimination in multispectral wide-field images

In this paper we present an automated method for classifying astronomical objects in multi-spectral widefield images. The classification method is divided into three main stages. The first one consists of locating and matching the astronomical objects in the multi-spectral images. In the second stage we create a compact representation of each object applying principal component analysis to the images. In the last stage we classify the astronomical objects using locally weighted linear regression and a novel oversampling algorithm to deal with the unbalance that is inherent to this class of problems. Our experimental results show that our method performs accurate classification using small training sets and in the presence of significant class unbalance.