TanDEM-X High Resolution DEMs and Their Applications to Flow Modeling

Lava flow modeling can be a powerful tool in hazard assessments; however, the ability to produce accurate models is usually limited by a lack of high resolution, up-to-date Digital Elevation Models (DEMs). This is especially obvious in places such as Kilauea Volcano (Hawaii), where active lava flows frequently alter the terrain. In this study, we use a new technique to create high resolution DEMs on Kilauea using synthetic aperture radar (SAR) data from the TanDEM-X (TDX) satellite. We convert raw TDX SAR data into a geocoded DEM using GAMMA software [Werner et al., 2000]. This process can be completed in several hours and permits creation of updated DEMs as soon as new TDX data are available. To test the DEMs, we use the Harris and Rowland [2001] FLOWGO lava flow model combined with the Favalli et al. [2005] DOWNFLOW model to simulate the 3-15 August 2011 eruption on Kilauea's East Rift Zone. Results were compared with simulations using the older, lower resolution 2000 SRTM DEM of Hawaii. Effusion rates used in the model are derived from MODIS thermal infrared satellite imagery. FLOWGO simulations using the TDX DEM produced a single flow line that matched the August 2011 flow almost perfectly, but could not recreate the entire flow field due to the relatively high DEM noise level. The issues with short model flow lengths can be resolved by filtering noise from the DEM. Model simulations using the outdated SRTM DEM produced a flow field that followed a different trajectory to that observed. Numerous lava flows have been emplaced at Kilauea since the creation of the SRTM DEM, leading the model to project flow lines in areas that have since been covered by fresh lava flows. These results show that DEMs can quickly become outdated on active volcanoes, but our new technique offers the potential to produce accurate, updated DEMs for modeling lava flow hazards.

POSE ESTIMATION AND 3D RECONSTRUCTION USING SENSOR FUSION

A camera maps 3-dimensional (3D) world space to a 2-dimensional (2D) image space. In the process it loses the depth information, i.e., the distance from the camera focal point to the imaged objects. It is impossible to recover this information from a single image. However, by using two or more images from different viewing angles this information can be recovered, which in turn can be used to obtain the pose (position and orientation) of the camera. Using this pose, a 3D reconstruction of imaged objects in the world can be computed. Numerous algorithms have been proposed and implemented to solve the above problem; these algorithms are commonly called Structure from Motion (SfM). State-of-the-art SfM techniques have been shown to give promising results. However, unlike a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) which directly give the position and orientation respectively, the camera system estimates it after implementing SfM as mentioned above. This makes the pose obtained from a camera highly sensitive to the images captured and other effects, such as low lighting conditions, poor focus or improper viewing angles. In some applications, for example, an Unmanned Aerial Vehicle (UAV) inspecting a bridge or a robot mapping an environment using Simultaneous Localization and Mapping (SLAM), it is often difficult to capture images with ideal conditions. This report examines the use of SfM methods in such applications and the role of combining multiple sensors, viz., sensor fusion, to achieve more accurate and usable position and reconstruction information. This project investigates the role of sensor fusion in accurately estimating the pose of a camera for the application of 3D reconstruction of a scene. The first set of experiments is conducted in a motion capture room. These results are assumed as ground truth in order to evaluate the strengths and weaknesses of each sensor and to map their coordinate systems. Then a number of scenarios are targeted where SfM fails. The pose estimates obtained from SfM are replaced by those obtained from other sensors and the 3D reconstruction is completed. Quantitative and qualitative comparisons are made between the 3D reconstruction obtained by using only a camera versus that obtained by using the camera along with a LIDAR and/or an IMU. Additionally, the project also works towards the performance issue faced while handling large data sets of high-resolution images by implementing the system on the Superior high performance computing cluster at Michigan Technological University.