Object-Based Building Extraction from High Resolution Satellite Imagery
R. Attarzadeh * *, M. Momeni®
* Islamic Azad University, Maybod branch, Maybod, Yazd, Iran- r.attarzadeh@gmail.com
? Department of Surveying Engineering, Faculty of Engineering, University of Isfahan, Hezar Jerib Ave., Isfahan, Iran
Commission IV, WG IV/2
KEY WORDS: Extraction, Classification, Detection, Object, Algorithm, High Resolution
ABSTRACT:
Automatic building extraction from high resolution satellite imagery is considered as an important field of research in remote sensing
and machine vision. Many algorithms for extraction of buildings from satellite images have been presented so far. These algorithms
mainly have considered radiometric, geometric, edge detection and shadow criteria approaches to perform the building extraction. In
this paper, we propose a novel object based approach for automatic and robust detection and extraction of building in high spatial
resolution images. To achieve this goal, we use stable and variable features together. Stable features are derived from inherent
characteristics of building phenomenon and variable features are extracted using SEparability and THresholds analysis tool. The
proposed method has been applied on a QuickBird imagery of an urban area in Isfahan city and visual validation demonstrates that
the proposed method provides promising results.
1. INTRODUCTION
Automatic building extraction from high resolution satellite
imagery is considered as an important field of research in
remote sensing and machine vision. So far, many algorithms
have been presented for the extraction of buildings from
satellite image. These algorithms have mainly considered
radiometric, geometric, edge detection and shadow criteria
approaches. With the advent of high resolution satellite
imagery, a new source has been provided for building extraction
algorithms. Such spatial resolution clarifies a large number of
details in urban area that have facilitated extraction of urban
phenomenon such as roads and buildings.
Since in the high spatial resolution satellite imagery, the pixel
size is much smaller than the authentic size of the building, the
group of pixels represent a building. Therefore the analysis
taken into consideration should be object based image analysis
which is more prominent today, rather than pixel-based
approach. Object Based Image Analysis allows us to use the
other spectral information such as average value of each band,
minimum and maximum value, variance along with spatial
information such as distance, neighborhood and topology by
considering image objects which results from the segmentation
as processing unit (Blaschke, 2010).
Here, the human visual system acts on the basis of
understanding typical patterns and their relation with real
objects. In addition to gray value in this system, such other
features as texture, shape, size, and inter-objects relationships
are effective in developing these patterns. A similar
interpretational approach is used in object based image analysis
of remote sensing imagery, though accessing complexity and
workmanship of human perception is not possible (Nussbaum et
al., 2008).
Due to the numerous limitations in only using spectral features
for building extraction, particularly where there is a spectral
* Corresponding author.
overlap between building and other urban phenomena, object-
based image analysis seems necessary because of taking spatial,
contextual, and geometric concepts into account.
Several studies have been conducted in relation to building
extraction from high resolution satellite imagery using object
based image analysis. One of the first studies was carried out by
Hofmann in 2001. In this study the object based image analysis
is used to extract the informal settlement of Cape Town from
Ikonos image. Image segmentation and successive classification
of image objects have been done by complex class hierarchy
using different features such as shape, size, context and texture.
The same technique is used in more enhanced and improved
way to extract an informal settlement of Rio de Janerio. The
method is used in a more simplified class hierarchy in a
Quickbird image with fuzzy membership function and their
combinations (Hofmann et al., 2008).
In another research a Quickbird image was used for object
based image analysis to extract urban features for making a
building inventory of Bangkok city (Dutta and Serker, 2005).
Multi level segmentation approach was used to detect different
urban object in appropriate size and the segments are classified
in a hierarchical scheme. Fuzzy membership function is applied
to utilize different shape, size and spectral feature
characteristics for building detection.
In another study, a VHR orthoimage is used to extract the very
dense urban slum dwellings and population living in it is
estimated (Aminipouri et al., 2009). Chessboard segmentation is
applied first to reduce the area of the interest and then
multiresolution segmentation is used for efficient and
meaningful segmentation of the image to carry out classification
of buildings. Fuzzy membership and nearest neighbour
classification are applied for extraction of different types of roof
structures based on colour, shape, size, texture and context.
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