Astrobiologists are overwhelmed by the huge volume of images from other planets. Now they have help in the form of a system that automatically identifies objects of interest in geological images
The
search for life on other planets is hotting up. The seemingly endless
train of Mars rovers have found convincing evidence of a warmer and
wetter climate on Mars. The Huygens and Cassini spacecraft have found
lakes, beaches, rivers and rain on Titan (albeit of the the oily
variety). And Europa’s dark, warm ocean looks increasingly inviting for
astrobiologists.
Then there are the ever-increasing
hordes of exoplanets in the habitable zones around other stars.It’s
never been a better time to be an astrobiologist.
One
problem that this new breed of scientist faces is data overload. Each
image from Mars has to be pored over by a human expert before the
rover’s next move can be planned and executed.
And since
these images are increasingly numerous, this is a time consuming task.
So a way to automate the classification of these images, at least
partially, would be hugely useful.
Step forward
Patrick McGuire at the Freie Universität in Berlin, Germany and a few
pals who have built and tested an automated system that does just this.
They call their new system the cyborg astrobiologist.
The
new system is relatively simple. It consists of a Samsung Propel
smartphone, which has a camera capable of taking 1280 x 960 pixel
images, connected by bluetooth to a Dell Inspiron 9300 laptop. For the
moment, it requires a human helper to carry and point the camera but
it’s not hard to imagine how the system could be fitted to an autonomous
rover.
The phone takes photos of the terrain as it
moves around, sending them it to the laptop for analysis. This where the
clever part takes place.
The laptop analyses each photo
by comparing it earlier images it has received and looking for
similarities between them. It analyses the colour of the scene and the
texture to calculate a similarity score.
In that way, it
classifies images of similar rocks and groups them together. This same
process also reveals when the images differ significantly, indicating
that terrain has changed or that an object of interest has appeared in
the scene. At this point, the system alerts a human astrobiologist who
can take over and analyse the novel features in more detail.
That’s
handy because the system does not need to know what type of rock it is
looking at but is still able to spot when things get interesting.
These
guys tested the system on rocky outcrops in the coalfields of West
Virginia and say it is impressive. “The image-matching procedure of this
system performed very well…giving a 91% accuracy for similarity
detection,” say McGuire and co.
That’s a potentially
useful device that could make life much easier for astrobiologists both
on Earth and further afield. For example, it could significantly reduce
the amount of data a Mars rover would have to send to Earth for analysis
and therefore dramatically speed up a rover’s work.
“This
image-compression technique could be useful in giving more scientific
autonomy to robotic planetary rovers, and in assisting human astronauts
in their geological exploration and assessment,” they say.
Astrobiologists have never had it so good. But with systems like this in the works, they could soon have it even better.
Ref: arxiv.org/abs/1309.4024: The Cyborg Astrobiologist: Matching of Prior Textures by Image Compression for Geological Mapping and Novelty Detection
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