Astronomical object detection with a robust hit-or-miss transform

Astronomical object detection is a particularly difficult but very challenging task. Indeed, astronomical images may contain a high noise level due to huge distance within the Universe or to the low photon flow collected on telescope mirrors [1]. Some astronomical objects of interest such as Low Surface Brightness (LSB) galaxies are characterized by a very low signal-to-noise ratio and thus are rather extracted manually and then are sometimes lost or not detected. In this paper, we propose an automatic approach using Mathematical Morphology, well-known for its appropriate care of spatial information (shape and luminance profile of LSB galaxies is known). In order to be able to detect objects in noisy environments, we propose a new morphological operator for template matching, namely a robust hit-or-miss transform.