An improved method for object detection in astronomical images

Abstract

This paper introduces an improved method for detecting objects of interest (galaxies and stars) in astronomical images. After applying a global detection scheme, further refinement is applied by dividing the entire image into several irregularly sized sub-regions using the watershed segmentation method. A more refined detection procedure is performed in each sub-region by applying adaptive noise reduction and a layered strategy to detect bright objects and faint objects, respectively. Finally, a multi-threshold technique is used to separate blended objects. On simulated data, this method can detect more real objects than SEXTRACTOR at comparable object counts (91 per cent versus 83 per cent true detections) and has an increased chance of successfully detecting very faint objects, up to 2 mag fainter than SEXTRACTOR on similar data. Our method has also been applied to real observational image data sets to verify its effectiveness.