Asteroid Hunter

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ASTEROID HUNTER

StarIdentification Using Matching Probability

Destin Smith-Norris

CAP4401 [email protected] 23, 2009


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Objectives

To accurately identify individual stars in animage

To use these stars to detect objects which

are not recognizable Do this using only the basic information able

to be calculated - the stars'relative brightness, relative location to

other stars, and number of near neighbors


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Relevance

For my project, to attempt to locateasteroids in an amateur astrophotopher'simage

In the real world, star identification is criticalfor space based devices to calculate theirattitude, or location, in Space

Without accurate positioning in Space it

would be impossible to keep satellites inOrbit


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Algorithm Used

There are many popular starID algorithmsused in Space, however there was onerecently ,published by Xie Jungeng Jiang

Wanshou and Gong Jianya using matchingprobability

The success of my application depends onthree assumptions

1)that there will beenough nearby stars to

compare against,2)that the brightest neighbor to the base star will

be the same in the user image and thedatabase

3)that there is minimal distortion within the image


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Algorithm

1.The algorithm as implemented takesboth the database image and theuser supplied image and creates for

each a matrix of all the star locationssorted by brightness

2.Then, for each star the program findsall the neighbors and calculates the

angular distances by pixel and storesthis along with the location

3.Finally, the ratio of the angular distanceof each base star to every one of its

neighbors is calculated using thenext brightest neighbor as a


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Algorithm Cont


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Algorithm Cont

The next step is to loop throughevery star in the user imagedatabase and compare the ratios ofeach base star to every star in thedatabase

When two ratios are almost the

same, they are considered amatch. When two stars haveenough matches, they areconsidered to be the same star

and its coordinates are recorded


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Results

Using my sample images results inmany false star identification. Thisis because, I have determined, the

fact that my camera isuncalibratedto the database

This means that the ratios do notmatch up accurately

To test this theory, I used myapplication to test matching asubsection of an image to thewhole and it succeeds in accuratelyidentifying the stars


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Original User Image


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Matches in Full User Image


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Matches in Cropped Image


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Results

Using a threshold of .001, aminimum of 6 probable matches,and a neighborhood of 100 pixels, I

got these results.

27 identified stars, out of 103 starsin the cropped image.

Using this user supplied imageagainst a database imageextracted from Stellarium gives atmost a few correctly identifiedstars.


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Conclusion

I believe if used with calibrated equipmentand a database designed for the particularcamera, this algorithm would be

completely feasible for use in a real-worldmission critical application. While my application failed to accomplish

the goals for the Asteroid Hunter testing

application, it did succeed in testing theoverall validity of the probability matchingalgorithm.


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Source

Xie Junfeng, JiangWanshou, and Gong Jianya. Anew star identification algorithm based onmatching probability. In Geoscience and RemoteSensing Symposium, 2008. IGARSS 2008. IEEE

International, volume 3, pages III 1166III 1169, July 2008.

Asteroid Hunter

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