A Boosting approach for the detection of faint compact sources in wide
field aperture synthesis radio images

VICOROB
http://vicorob.udg.edu

Albert Torrent ¹, Marta Peracaula¹, Xavier Lladó¹, Jordi Freixenet¹, Juan Ramón
Sánchez-Sutil², Josep M. Paredes³ and Josep Martí²¹Universitat de Girona, ²Universidad de Jaen, ³Universitat de Barcelona{atorrent,martapb,llado,jordif}@eia.udg.edu, {jrssutil,jmarti}@ujaen.es, jmparedes@ub.edu

Universidad de Jaén

Abstract

1. Motivation

Several thresholding techniques have been proposed so far in order to perform faint compact source detection
in wide field interferometric radio images. Due to their low intensity/noise ratio, some objects can be easily
missed by these automatic detection methods. In this paper we present a novel approach to overcome thisproblem. Our proposal is based on using local features extracted from a bank of filters. These features provide
a description of different types of faint source structures. Our approach performs an initial training step in order
to automatically learn and select the most salient features, which are then used in a Boosting classifier toperform the detection. The validity of our method is demonstrated using 19 images that compose a 2.5 deg x
2.5 deg radio mosaic, obtained with the Giant Metrewave Radio Telescope, centered on the MGRO J2019+37
peak of gamma emission at the Cygnus region. A comparison with two previously published radio catalogues ofthis region (task SAD of AIPS and SExtractor) is also provided.

• Recent wide field radiointerferometric surveys show a large amountof faint compact objects with intensities very near to noise levels.
• The images are usually very complex with the presence of diffused
extended emission and interferometric patterns.
• Automated detection methods working at low signal-to-noise ratios
are necessary in order to create reliable catalogues of the compactsources.

1. Dictionary: We generate a dictionary of words which defines what a source is. A word iscomposed by a filtered patch of a source and the filter used.

2. Boosting detection

Bank offilters

GroundTruth

DICTIONARY
Filtered patches

Extract features

Dictionary
datasetTraining
dataset d11 d 1n...
...

d m1  d mn PatchFilter

Featureselection
Boosting

2. Training: We characterize a set of training images using the normalized cross correlationof the filtered images with the dictionary words. Some positive points (sources) andnegative points (non sources) are selected to perform the training by using a Boostingclassifier.
Feature = (image filter)
patch

*

TEST
Feature 1:
Classifier

TestingdatasetDictionary
Word=*TRAINING

Patches selected fromtrue positive sources

Filteredimages(

3. Testing: We combine all theweak classifiers obtained fromthe Boosting training to performthe source detection.

)*Classifier 1=

Classifier 2+Detection
results

Classifier N+…+=

3. Application to a set of images

4. Experimental results

To illustrate our method we use the 19
deep radio images obtained by Paredes
et al. (A&A, accepted July 2009) at 610
MHz (49 cm) to survey a 2.5°x2.5°
region centered on the MGRO J2019+37
peak of high energy gamma-rays
emission. Each of the images covers a28’ radius circular region and they are
partly overlapped in a hexagonal pattern
to compose the final mosaic.
These images are ideal for testingautomated detection methods because:- They show a significant amount ofdetail due to its high spatial dynamic
range (over 1000).
- They have a remarkable population of compact sources (i.e. star-like objects)
and show extended diffuse emission.
- They show unwanted interferometric pattern mainly caused by deconvolution
artifacts and grating rings from strong sources both inside and outside theprimary beam.

The results presented have been obtained using one field to generate thedictionary and six different fields to train the classifier.

5. Conclusions
Our Boosting approach succeeds in reducing the number of FP with respect toSAD (information not available for SExtractor), while providing a similar numberof TP. However, some of these TP are not coincident in position:• Our approach finds 368 TP which coincide with the results reported by SAD(80%) and 402 TP with respect to those results reported by SExtractor (85%).
• 321 TP are coincident in all three methods (Boosting, SAD and SExtractor).
In summary, the results reported in this work show the validity of our Boosting
approach to perform the automated detection of faint objects.

Results from field 15

Results from field 14

We compare our results with two catalogues: the first one was produced byParedes et al. 2008 (private communication) using SAD task of AIPS. The second
one was obtained using SExtractor published by Paredes et al. (A&A, accepted
July 2009).
For a better comparison of our method with these two catalogues we used thesame exclusion zones. These zones generally contain a great amount of artifacts
near the bright sources.
The following table shows the symbol code used in the figures and the number ofTrue Positive (TP) and False Positive (FP) detections found in each work:

BoostingSAD
SExtractor

TP detections
○ 505
□ 455
∆ 473

FP detections

x 96+ 474
Not Available

Classification of true and false positive has been performed by close visual inspection by theauthor of each work.
Acknowledgements: The authors acknowledge support by DGI of the Spanish Ministerio de Educación y Ciencia (MEC) under grants AYA2007-68034-C03-01, AYA2007-68034-C03-02 and AYA2007-68034-C03-03, as well as
partial support by the European Regional Development Fund (ERDF/ FEDER). GMRT is run by the National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research.