Analysis of a set of multiband images in the framework of a Virtual Observatory
by
Albert Bijaoui
Laboratoire Cassiopée UMR 6202, Observatoire de la Côte d'Azur, BP 4229, 06304 Nice Cedex 4 (France)
Coauthors: Christophe Benoist, Anais Guennec, Eric Slezak, Laboratoire Cassiopée UMR 6202, Observatoire de la Côte d'Azur, BP 4229, 06304 Nice Cedex 4 (France)

In the framework of a Virtual Observatory a same sky region may be observed with a high number of spectral bands. Even for few bands, it is not easy to match the objects identified in each image and to get consistent measurements. An approach based on a fusion image is proposed. Different algorithms were tested for building such an image. The best results are obtained from the one which takes into account a deconvolution with a wavelet approach.

Multiscale algorithms allow us to increase the detection rate compared to classical ones based on a pixel thresholding. The measurements of all the objects are then done from the radial profiles. This allows us to get consistent measurements from images obtained with different filters.

The main difficulty lies in the photometrical classification. The number of significant classes increases very rapidly with the number of bands. Principal component analysis, based on photometrical correlations, does not allow us to reduce the number of classes by an important factor. Instead to search to identify all the photometrical classes, it is possible to describe the spectral distribution as a superimposition of different pure element ones. This mixing can be due either to a true object superimposition or to the description of an intermediate class as the interpolation between different pure elements. The proposed method starts from a classification built from photometrical ratios. The source separation is then done by a CLEAN-like algorithm.

Date received: March 1, 2005