Graduation Year
2012
Document Type
Thesis
Degree
M.S.C.S.
Degree Granting Department
Computer Science and Engineering
Major Professor
Lawrence Hall, Ph.D.
Co-Major Professor
Dmitry Goldgof, Ph.D.
Committee Member
Frank Muller-Karger, Ph.D.
Keywords
Fuzzy C-Means, Clustering, Texture, Sun Glint, Deepwater Horizon
Abstract
The release of crude oil or other petroleum based products into marine habitats can have a devastating impact on the environment as well as the local economies that rely on these waters for commercial fishing and tourism. The Deepwater Horizon catastrophe that started on April 20th 2010 leaked an estimated 4.4 million barrels of crude oil into the Gulf of Mexico over a 3 month period threatening thousands of species and crippling the gulf coast. The National Oceanic and Atmospheric Administration (NOAA) used several satellite remote sensing technologies to manually track and predict the extent and location of oil on the surface of the gulf waters. This thesis proposes a methodology to automatically identify surface oil using an unsupervised clustering algorithm an compares the discovered regions of oil to the reports generated by NOAA during the incident. The fuzzy c-means clustering algorithm is used to partition the satellite image pixels into groups that represent either oil or not oil. A variety of MODIS data features and image analyzing techniques have been explored to produce the most accurate set of regions.
Scholar Commons Citation
Kidd, Joshua, "Detecting Surface Oil Using Unsupervised Learning Techniques on MODIS Satellite Data" (2012). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/4098
