<p>Accurate 3D landscape models of cities or mountains have wide applications in mission</p>
<p>planning, navigation, geological studies, etc. Lidar scanning using drones can provide high</p>
<p>accuracy 3D landscape models, but the data is more expensive to collect as the area of</p>
<p>each scan is limited. Thanks to recent maturation of Very-High-Resolution (VHR) optical</p>
<p>imaging on satellites, people nowadays have access to stereo images that are collected on a</p>
<p>much larger area than Lidar scanning. My research addresses unique challenges in satellite</p>
<p>stereo, including stereo rectification with pushbroom sensors, dense stereo matching using</p>
<p>image pairs with varied appearance, e.g. sun angles and surface plantation, and rasterized</p>
<p>digital surface model (DSM) generation. The key contributions include the Continuous 3D-</p>
<p>Label Semi-Global Matching (CoSGM) and a large scale dataset for satellite stereo processing</p>
<p>and DSM evaluation.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/21685427 |
| Date | 08 December 2022 |
| Creators | Sonali D Digambar Patil (14228030) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/thesis_pdf/21685427 |
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