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.
Details
Title
- 3D rooftop detection and modeling using orthographic aerial images
Contributors
- Khanna, Kunal (Author)
- Femiani, John (Thesis advisor)
- Wonka, Peter (Thesis advisor)
- Razdan, Anshuman (Committee member)
- Maciejewski, Ross (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2013
Subjects
Resource Type
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Note
- Partial requirement for: M.S., Arizona State University, 2013Note typethesis
- Includes bibliographical references (p. 48-50)Note typebibliography
- Field of study: Computer science
Citation and reuse
Statement of Responsibility
by Kunal Khanna