NASA Spinoff

Star-Mapping Tools Enable Tracking of Endangered Animals

Originating Technology/NASA Contribution

Try this: Print out a lower-case letter “o” in Times New Roman, 10-point font. Now hold the paper at arm’s length. Viewed from this distance, the area inside the “o” is approximately equal to the area observed in the Hubble Ultra Deep Field, an image taken by the Hubble Space Telescope. Within that space—only one thirteen-millionth of the sky’s total area—Hubble revealed approximately 10,000 galaxies, each containing billions of stars. The observable universe as a whole contains some 80 billion galaxies and anywhere between 30 and 70 billion trillion stars.

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In order to map the intimidating fields of stars Hubble—among other sensitive telescopes—would uncover following its launch in 1990, astronomers needed a powerful tool for comparing and matching star configurations within the telescope’s collected images. In 1986, Princeton University physics professor Edward J. Groth, supported by the Hubble Space Telescope program, invented a pattern-matching algorithm that addressed the challenge. The Groth algorithm forms triangles between every possible triplet of stars in an image (each star’s position being represented by an x-y coordinate pair). It then compares the triangles’ measurements to those in other images, determining any matches. Because certain properties of a triangle do not change if the triangle is rotated or altered in size, the algorithm allows astronomers to effectively map star locations using images of different magnification and orientation.

The Groth algorithm was incorporated into the Space Telescope Science Data Analysis System (STSDAS), a software suite for calibrating, analyzing, and reducing data gathered by Hubble. An updated version of the algorithm is still part of STSDAS, which is available to the public from the Space Telescope Science Institute, a NASA partner located in Baltimore, involved with management of Hubble and the currently under-development James Webb Space Telescope.

Partnership

In 2002, Portland, Oregon, software programmer and technical writer Jason Holmberg had a rare encounter with a whale shark while scuba diving in the Red Sea. Growing to lengths of up to 40 feet, the filter-feeding whale shark is the world’s largest—and among its least understood—fish species. It is also listed as vulnerable to extinction by the International Union for Conservation of Nature. Fascinated, Holmberg began toying with ideas to help improve methods for tracking the unusual fish. (Physically tagging the sharks for satellite tracking proves inefficient because the tags are frequently lost or rendered inoperative within weeks or a few months; with plastic visual tags, less than 1 percent are spotted again after tagging.) Holmberg eventually teamed­ with marine biologist Brad Norman, founder of the Perth, Australia, nonprofit ECOCEAN, which studies the whale sharks that migrate annually through Western Australia’s Ningaloo Marine Park. Norman had been tracking the Ningaloo sharks by photographing and identifying each shark from the distinctive white spots on its skin, a marker as unique as fingerprints are in humans. The process was a tedious one; Norman had to examine and compare all photos by eye.

For help conceiving a computer-based tracking system, Holmberg turned to friend Dr. Zaven Arzoumanian, an astrophysicist with Universities Space Research Association on contract at Goddard Space Flight Center, who contributed to the undertaking as a side project independent of his NASA duties. The pair were contemplating the challenge of identifying whale sharks in an automated way when they encountered the Groth algorithm, which provided an ideal basis for creating a pattern-matching program using shark spots instead of stars in the universe. (Fittingly, the whale shark’s name in Madagascar, “marokintana,” means “many stars,” while “geger lintang,” its Javanese name, means “stars in the back.”) Holmberg and Arzoumanian modified the algorithm, applying a technique called blob extraction to pinpoint single-color pixel groups—a useful tweak since the whale shark’s spots are larger and more irregular in shape than stars in photographs. They also introduced rotation correction and contrast enhancement elements to clarify photos, compensate for perspective issues (the sharks are rarely photographed in perfect profile), and take into account 3-D qualities not present in space images.