Object detection is one of the most important procedures in astronomical imaging. This paper deals with segmentation of astronomical images based on random forrest classifier. We consider astronomical image data acquired using a photometric system with B, V, R and I filters. Each image is acquired in more realizations. All image realizations are corrected using master dark frame and master at field obtained as an average of hundreds of images. Then a profile photometry is applied to find possible position of stars. The classifier is trained by B, V, R and I image vectors. Training samples are defined by user using ellipsoidal regions (20 selections for both classes: object, background). A number of objects and their positions are compared with astronomical object catalogue using Euclidean distance. We can conclude that the performance of the presented technique is fully comparable to other SoA algorithms.

Date Published: 23 September 2014
PDF: 6 pages
Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 921722 (23 September 2014); doi: 10.1117/12.2062009

Published in SPIE Proceedings Vol. 9217:
Applications of Digital Image Processing XXXVII
Andrew G. Tescher, Editor(s)