Full metadata
Title
3D rooftop detection and modeling using orthographic aerial images
Description
Detection of extruded features like rooftops and trees in aerial images automatically is a very active area of research. Elevated features identified from aerial imagery have potential applications in urban planning, identifying cover in military training or flight training. Detection of such features using commonly available geospatial data like orthographic aerial imagery is very challenging because rooftop and tree textures are often camouflaged by similar looking features like roads, ground and grass. So, additonal data such as LIDAR, multispectral imagery and multiple viewpoints are exploited for more accurate detection. However, such data is often not available, or may be improperly registered or inacurate. In this thesis, we discuss a novel framework that only uses orthographic images for detection and modeling of rooftops. A segmentation scheme that initializes by assigning either foreground (rooftop) or background labels to certain pixels in the image based on shadows is proposed. Then it employs grabcut to assign one of those two labels to the rest of the pixels based on initial labeling. Parametric model fitting is performed on the segmented results in order to create a 3D scene and to facilitate roof-shape and height estimation. The framework can also benefit from additional geospatial data such as streetmaps and LIDAR, if available.
Date Created
2013
Contributors
- Khanna, Kunal (Author)
- Femiani, John (Thesis advisor)
- Wonka, Peter (Thesis advisor)
- Razdan, Anshuman (Committee member)
- Maciejewski, Ross (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
viii, 50 p. : ill. (some col.)
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.17935
Statement of Responsibility
by Kunal Khanna
Description Source
Viewed on Dec. 9, 2013
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2013
Note type
thesis
Includes bibliographical references (p. 48-50)
Note type
bibliography
Field of study: Computer science
System Created
- 2013-07-12 06:24:39
System Modified
- 2021-08-30 01:41:30
- 2 years 7 months ago
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