X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM
This work addresses the need for accurate point source detection in astronomy, but it appears incremental as it builds on existing SVM-based methods.
The paper tackled the problem of recognizing point sources in X-ray astronomical images by proposing a granular binary-tree SVM classifier, achieving an accuracy of around 89% in experiments.
The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak detection on the image. The image and spectral features of these potential point sources are then extracted. Finally, a classifier to recognize the true point sources is build through the extracted features. Experiments and applications of our approach on real X-ray astronomical images are demonstrated. comparisons between our approach and other SVM-based classifiers are also carried out by evaluating the precision and recall rates, which prove that our approach is better and achieves a higher accuracy of around 89%.