Dissertation Announcement for Ramakalavathi Marapareddy
10/19/15 at 10:30 AM
October 5, 2015
Dear faculty, graduate and undergraduate students, You are cordially invited to my Ph.D. dissertation oral defense.
Dissertation Title: Levee Slide Detection Using Synthetic Aperture Radar Magnitude and Phase
When: Monday, October 19, 2015, 10:30 am
Where: HPC, Room: 10
Candidate: Ramakalavathi Marapareddy
Degree: Ph.D., Electrical and Computer Engineering Committee:
Dr. Nicolas H. Younan
Professor, Head of Electrical and Computer Engineering Dept., and James Worth Bagley Chair
(Major Professor)
Dr. James V. Aanstoos
Associate Research Professor, Geosystems Research Institute
(Committee Member)
Dr. Jenny Q. Du
Professor of Electrical and Computer Engineering and Bobby Shackhouls Endowed Professorship
(Committee Member)
Dr. Hyeona Lim
Associate Professor of Mathematics
(Committee Member)
Dr. Jonathan R. Woody
Assistant Professor of Statistics
(Committee Member)
Abstract:
The objectives of this research are to support the development of state-of-the-art methods using remotely sensed data to detect slides or anomalies in an efficient and cost-effective manner based on the use of SAR technology. Slough or slump slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events. This work investigates the facility of detecting the slough slides on an earthen levee with different types of polarimetric Synthetic Aperture Radar (polSAR) imagery. The source SAR imagery is fully quad-polarimetric L-band data from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area encompasses a portion of the levees of the lower Mississippi river, located in Mississippi, United States. The obtained classification results reveal that the polSAR data unsupervised classification with features extraction produces more appropriate results than the unsupervised classification with no features extraction. Obviously, supervised classification methods provide better classification results compared to the unsupervised methods. The anomaly identification is good with these results and was improved with the use of a majority filter. The classification accuracy is further improved with a morphology filter. The classification accuracy is significantly improved with the use of GLCM features. The classification results obtained for all three cases (magnitude, phase, and complex data), with classification accuracies for the complex data being higher, indicate that the use of synthetic aperture radar in combination with remote sensing imagery can effectively detect anomalies or slides on an earthen levee. For all the three samples it consistently shows that the accuracies for the complex data are higher when compared to those from the magnitude and phase data alone. The tests comparing complex data features to magnitude and phase data alone, and full complex data, and use of post-processing filter, all had very high accuracy. Hence we included more test samples to validate and distinguish results.
Kind Regards,
Ramakalavathi Marapareddy