Abstract
dc:description.abstract<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>
Degree
thesis:*- 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 and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Buskirk, Adam
- Contributors dc:contributor
-
- Yumpeng Pan
Subjects
dc:subject × 5Rights
dc:rights- Language dc:language
- en
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://openprairie.sdstate.edu/etd/959
- OAI identifier oai:identifier
- oai:openprairie.sdstate.edu:etd-1962