Model based building extraction from high resolution aerial images

Download

index.pdf

Date

2004

Author

Bilen, Burak

Metadata

Show full item record

Item Usage Stats

141
views

56
downloads

A method for detecting the buildings from high resolution aerial images is proposed. The aim is to extract the buildings from high resolution aerial images using the Hough transform and the model based perceptual grouping techniques.The edges detected from the image are the basic structures used in the building detection procedure. The method proposed in this thesis makes use of the basic image processing techniques. Noise removal and image sharpening techniques are used to enhance the input image. Then, the edges are extracted from the image using the Canny edge detection algorithm. The edges obtained are composed of discrete points. These discrete points are vectorized in order to generate straight line segments. This is performed with the use of the Hough transform and the perceptual grouping techniques. The straight line segments become the basic structures of the buildings. Finally, the straight line segments are grouped based on predefined model(s) using the model based perceptual grouping technique. The groups of straight line segments are the candidates for 2D structures that may be the buildings, the shadows or other man-made objects. The proposed method was implemented with a program written in C programming language. The approach was applied to several study areas. The results achieved are encouraging. The number of the extracted buildings increase if the orientation of the buildings are nearly the same and the Canny edge detector detects most of the building edges.If the buildings have different orientations,some of the buildings may not be extracted with the proposed method. In addition to building orientation, the building size and the parameters used in the Hough transform and the perceptual grouping stages also affect the success of the proposed method.

Subject Keywords

Study and Teaching.

URI

http://etd.lib.metu.edu.tr/upload/3/12604984/index.pdf
https://hdl.handle.net/11511/14201

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Building Detection in satellite images by textural features and Adaboost CETIN, MELIH; Halıcı, Uğur (2010-08-24) A method based on textural features and Adaboost for detecting buildings in satellite images is proposed. Several local textural features including mean and standard deviation of image intensity and gradient, Zernike moments, Circular-Mellin features, Haralick features, Fourier Power Spectrum, Wavelets, Gabor Filters, and a set features extracted from HSV color space are extracted. Adaboost learning algorithm is employed for both classification and determining the beneficial feature subset, due to its featu...
Hypothesis based detection of building with rectilinear projection in satellite images using shade and color information Güdücü, Volkan; Halıcı, Uğur (2010-12-01) A new hypothesis based method for detecting rectilinear buildings, that have ground projection made of combination of rectangles, in satellite images is proposed. While the hypotheses are established using the lines detected in the satellite images they are verified by using shadow and color segmentation information. The proposed method is implemented in MATLAB and tested in satellite images of different urban areas. The experimental results obtained are encouraging. ©2010 IEEE.
Building detection from satellite images using shadow and color information Güdücü, Hasan Volkan; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2008) A method for detecting buildings from satellite/aerial images is proposed in this study. The aim is to extract rectilinear buildings by using hypothesize first verify next manner. Hypothesis generation is accomplished by using edge detection and line generation stages. Hypothesis verification is carried out by using information obtained both from the color segmentation of HSV representation of the image and the shadow detection stages’ output. Satellite/aerial image is firstly filtered to sharpen the edges....
Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery Ok, Ali Ozgun; Senaras, Caglar; Yuksel, Baris (2013-03-01) This paper introduces a new approach for the automated detection of buildings from monocular very high resolution (VHR) optical satellite images. First, we investigate the shadow evidence to focus on building regions. To do that, we propose a new fuzzy landscape generation approach to model the directional spatial relationship between buildings and their shadows. Once all landscapes are collected, a pruning process is developed to eliminate the landscapes that may occur due to non-building objects. The fina...
Building Detection With Decision Fusion Senaras, Caglar; Ozay, Mete; Yarman Vural, Fatoş Tunay (Institute of Electrical and Electronics Engineers (IEEE), 2013-6) A novel decision fusion approach to building detection problem in VHR optical satellite images is proposed. The method combines the detection results of multiple classifiers under a hierarchical architecture, called Fuzzy Stacked Generalization (FSG). After an initial segmentation and pre-processing step, a large variety of color, texture and shape features are extracted from each segment. Then, the segments, represented in different feature spaces are classified by different base-layer classifiers of the F...

Citation Formats

B. Bilen, “Model based building extraction from high resolution aerial images,” M.S. - Master of Science, Middle East Technical University, 2004.