Lang Hong (Committee Member), Andrew Hsu (Other), Brian Rigling (Committee Member), Arnab Shaw (Advisor), Kefu Xue (Other)
Master of Science in Engineering (MSEgr)
Given the UHF bands properties of foliage and round penetration, a UHF-SAR image contains both above- and below-surface scatterers. The problem of detecting sub-surface objects is problematic due to the presence of above-surface scatterers in the detection images. In case of a single-pass anomaly image or a two-pass change image, the resulting anomalies or changes are due to scatterers above and below the surface, where the above surface anomalies/changes act as confusers. LIDAR digital elevation models (DEM) provide georegistered information about the above-surface objects present in the UHF-SAR scene. Detection of the above-surface objects in the LIDAR domain is used to rule out above-surface false-alarms in the UHF-SAR domain detection images. A complementary sensor fusion algorithm is implemented which exploits the limited ground penetrating capabilities of UHF-SAR and the false-alarm removal using LIDAR. For unitemporal and multitemporal UHF-SAR collections (both containing multiple-passes and multiple- polarizations) anomaly detection and change detection are implemented, respectively. In this thesis, various pixel-based and feature-based change detection algorithms are implemented to study the effectiveness of multitemporal change detection algorithms. In addition, incorporation of UHF-SAR multiple-passes and multiple-polarizations further improves detection results. The algorithms are tested using data collected under JIEDDOs Halite-1 program, which provides both UHF-SAR and LIDAR DEM.
Department or Program
Department of Electrical Engineering
Year Degree Awarded
Copyright 2012, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.