Detecting Faint Compact Sources Using Local Features and a Boosting Approach

Several techniques have been proposed so far in order to perform faint compact source detection in wide field interferometric radio images. However, all these methods can easily miss some detections or obtain a high number of false positive detections due to the low intensity of the sources, the noise ratio, and the interferometric patterns present in the images. In this paper we present a novel strategy to tackle this problem. Our approach is based on using local features extracted from a bank of filters in order to provide a description of different types of faint source structures. We then perform a training step in order to automatically learn and select the most salient features, which are used in a Boosting classifier to perform the detection. The validity of our method is demonstrated using 19 real images that compose a radio mosaic. The comparison with two well-known state of the art methods shows that our approach is able to obtain more source detections, reducing also the number of false positives.