Development of a Support-Vector-Machine-based Supervised Learning Algorithm for Land Cover Classification Using Polarimetric SAR Imagery

dc.contributor.authorBlack, James Noelen
dc.contributor.committeechairMili, Lamine M.en
dc.contributor.committeememberAbdel Hadi, Ahmed M.en
dc.contributor.committeememberZaghloul, Amir I.en
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2018-10-17T08:00:49Zen
dc.date.available2018-10-17T08:00:49Zen
dc.date.issued2018-10-16en
dc.description.abstractLand cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of SAR data. One fundamental step in any supervised learning classification algorithm is the selection and/or extraction of features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarimetric data is to decompose the data into the underlying scattering mechanisms. In this research, the Freeman and Durden scattering model is applied to ALOS PALSAR fully polarimetric data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the Freeman and Durden work, the classification capability of the model is assessed on amazon rainforest land cover types using a supervised Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined. Additionally, the performance of the median as a robust estimator in noisy environments for multi-pixel windowing is also characterized.en
dc.description.abstractgeneralLand type classification using Radar data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of Radar data. One fundamental step in any classification algorithm is the selection and/or extraction of discriminating features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarized Radar data is to decompose the data into the underlying scatter components. In this research, a scattering model is applied to real world data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the scattering model, the classification capability of the model is assessed on amazon rainforest land types using a Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined and compared using different estimators.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:17243en
dc.identifier.urihttp://hdl.handle.net/10919/85391en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSynthetic Aperture Radaren
dc.subjectSupport Vector Machineen
dc.subjectRobusten
dc.subjectPolarimetricen
dc.titleDevelopment of a Support-Vector-Machine-based Supervised Learning Algorithm for Land Cover Classification Using Polarimetric SAR Imageryen
dc.typeThesisen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen
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