Identifying Data Centers from Satellite Imagery

<p>We develop two different descriptors which can be utilized to describe satellite imagery. The first, the differential-magnitude and radius descriptor, describes a scene by computing the directional gradient of the scene with respect to a vector field whose solutions are circles around a pixel to be described, and then counts pixels in a descriptor matrix according to the magnitude of this gradient and the distance at which this magnitude occurs. The second, the radial Fourier descriptor, extracts from the scene a sequence of annuloid sectors, and uses this to approximate the behavior of the image on a circle around the point to be described. The fast Fourier transform is then used to obtain a description of this function in the frequency domain; the absolute values of these complex-valued frequencies form the descriptor. A set of data to test and perform parameter selection for these procedures using 79 Landsat 8 imagery scenes was constructed. A cellular evolutionary algorithm was then utilized for parameter selection by training and testing support vector machine classifiers using LIBSVM from this dataset utilizing classification accuracy as a fitness function. We then analyze the classification success associated with the two methods equipped with their optimized parameters.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Mathematics and Statistics

Year dc:date.available

2016

Author dc:creator

• Buskirk, Adam

Contributors dc:contributor

• Yumpeng Pan

• image recognition
• satellite imagery
• Mathematics
• Spatial Science
• Statistics and Probability

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

en

Repository record dc:identifier

https://openprairie.sdstate.edu/etd/959

OAI identifier oai:identifier

oai:openprairie.sdstate.edu:etd-1962