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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Buskirk, Adam"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis - Open Access","degree_discipline":"Mathematics and Statistics","degree_department":null,"school":null,"contributors":["Yumpeng Pan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T04:28:30Z","subjects":["image recognition","satellite imagery","Mathematics","Spatial Science","Statistics and Probability"],"languages":["en"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://openprairie.sdstate.edu/etd/959","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yumpeng Pan"]},{"key":"dc:creator","label":"Author","values":["Buskirk, Adam"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-05-10T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics and Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["image recognition","satellite imagery","Mathematics","Spatial Science","Statistics and Probability"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openprairie.sdstate.edu/etd/959"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Identifying Data Centers from Satellite Imagery"]}]}],"canonical_facts":{"dc:contributor":["Yumpeng Pan"],"dc:creator":["Buskirk, Adam"],"dc:date.available":["2016-05-10T07:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/959"],"dc:language":["en"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["image recognition","satellite imagery","Mathematics","Spatial Science","Statistics and Probability"],"dc:title":["Identifying Data Centers from Satellite Imagery"],"thesis:degree_discipline":["Mathematics and Statistics"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T04:28:30Z"}