Rectangular-shaped building detection from high resolution aerial/satellite
images is proposed for two different methods. Shadow information plays main role
in both of these algorithms. One of the algorithms is based on Hough
transformation, the other one is based on mean shift segmentation algorithm.
Satellite/aerial images are firstly converted to YIQ color space to be used in shadow
segmentation. Hue and intensity values are used in computing the ratio image which
is used to segment shadowed regions. For shadow segmentation Otsu&rsquo / s method is
used on the histogram of the ratio image. The segmented shadow image is used as
the input for both of two building detection algorithms. In the proposed methods,
shadowed regions are believed to be the building shadows. So, non-shadowed
regions such as roads, cars, trees etc. are discarded before processing the image. In
Hough transform based building detection algorithm, shadowed regions are firstly
segmented one by one and filtered for noise removal and edge sharpening. Then,
the edges in the filtered image are detected by using Canny edge detection
algorithm. Then, line segments are extracted. Finally, the extracted line segments
are used to construct rectangular-shaped buildings. In mean shift based building detection algorithm, image is firstly segmented by using mean shift segmentation
algorithm. By using shadow image and segmented image, building rooftops are
investigated in shadow boundaries. The results are compared for both of the
algorithms. In the last step a shadow removal algorithm is implemented to observe
the effects of shadow regions in both of two implemented building detection
algorithms. Both of these algorithms are applied to shadow removed image and
results are compared.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/12610179/index.pdf |
| Date | 01 December 2008 |
| Creators | Yalcin, Abdurrahman |
| Contributors | Ulusoy, Ilkay |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
Page generated in 0.002 seconds