Automatic 3D Earth Landscape Reconstruction by Satellite Stereo
Abstract
Accurate 3D landscape models of cities or mountains have wide applications in mission planning, navigation, geological studies, etc. Lidar scanning using drones can provide high accuracy 3D landscape models, but the data is more expensive to collect as the area of each scan is limited. Thanks to recent maturation of Very-High-Resolution (VHR) optical imaging on satellites, people nowadays have access to stereo images that are collected on a much larger area than Lidar scanning. My research addresses unique challenges in satellite stereo, including stereo rectification with pushbroom sensors, dense stereo matching using image pairs with varied appearance, e.g. sun angles and surface plantation, and rasterized digital surface model (DSM) generation. The key contributions include the Continuous 3D- Label Semi-Global Matching (CoSGM) and a large scale dataset for satellite stereo processing and DSM evaluation.
Degree
Ph.D.
Advisors
Guo, Purdue University.
Subject Area
Aerospace engineering
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