Document Type

Thesis - Open Access

Award Date

2016

Degree Name

Master of Science (MS)

Department / School

Mathematics and Statistics

First Advisor

Yumpeng Pan

Keywords

image recognition, satellite imagery

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.

Library of Congress Subject Headings

Remote sensing -- Data processing

Data libraries

Landsat satellites

Image analysis

Description

Includes bibliographical references (page 80-81)

Format

application/pdf

Number of Pages

93

Publisher

South Dakota State University

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Rights Statement

In Copyright