PE&RS June 2015 - page 424

June 2015
This special issue peeks under the hood
of the ongoing geospatial revolution that
began when we moved from film to digital
image capture. Suddenly, machines could
analyze photographs. They could count
pixels, compare their spectra, and map
change. Programmers created ways to
find patterns in the pixels that deviated
from the surrounding pixels by applying
various signal processing techniques. To
make sense of these patterns (or features),
programmers created libraries of objects that could be used as
filters to identify features within images and assign meaning
to them. Today, the quantity and variety of imagery continues
to rapidly grow, as do the tools of analysis, so the film-to-digital
revolution may still be in its infancy. The digital revolution is
currently morphing into the age of “big data.” To explain, let us
try a thought experiment.
Look around you. The space where we are is full of objects. We
know this because our eyes and brain work together to catalogue
the world into objects.. Take a picture of the same scene with a
digital camera and we get pixels, maybe megapixels, or even
gigapixels. We can search the photograph with our eyes and we
will notice things like houses, trees, and animals; if the image has
sufficient resolution we probably will not notice pixels, even though
they comprise the image and all the objects embedded within. This
is the concept of object-based image analysis in lay terms.
Whenwe lookarounduswemay see people, cars, keys, telephones,
chairs, lights, signs, insects, and so on. The objects we see depend
upon when and where we are. For example, we will see people in
a stadium during a ball game, but not cars. We will see cars stuck
in traffic during rush hour on the freeway, but not people. In both
cases we could ask questions like, “how many red pixels are in the
image?” But, what would that tell us? Or, we could ask, “how many
Cardinal fans are there at the game?” or “how many commuters are
on the road?” Should we count red pixels, red shirts, or red cars? If
shirts and cars, we are counting objects. When we add the context
of location we know even more. This is the concept of geographic or
geospatial object-based image analysis in lay terms.
These concepts are the gist of Geographic Object-Based Image
Analysis (GEOBIA). GEOBIA (pronounced gee-O’-bee-ah) is part
of the growth of the “big data” phenomenon. This matters because
how we look at the world governs what we can understand about
the world, and therefore what we can change in the world. Never
before has there been such diversity and complexity of geospatial
data as exists today. In this age of geospatial big data we need new
paradigms for extracting information from the data. GEOBIA is
such a paradigm, so I hope you will enjoy the technical articles in
this special issue.
Dr. Michael Hauck, ASPRS Executive Director
Dr. Michael Hauck
Russell G. Congalton
Technical Editor
Michael S. Renslow
Assistant Editor
Jie Shan
Assistant Director — Publications
Rae Kelley
Electronic Publications Manager/Graphic Artist
Matthew Austin
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