extraction of buildings in urban areas from very high resolution satellite images

7/28/2019 Extraction of Buildings in Urban Areas From Very High Resolution Satellite Images

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AUTOMATIC EXTRACTION OF BUILDINGS IN URBAN AREAS FROM VERY HIGH

RESOLUTION SATELLITE IMAGES

M. Ghanea a, *, P. Moallem b, M. Momeni a

a

Dept. of Surveying Eng., Faculty of Eng., University of Isfahan, Isfahan, Iran - (m.ghane, [email protected])b Dept. of Electrical Eng., Faculty of Eng., University of Isfahan, Isfahan, Iran - [email protected]

Session C1

KEY WORDS: Building Extraction, Very High Resolution (VHR) Satellite Images, Urban Area, K-means Clustering, Region

Growing, Boundary Improvement

ABSTRACT:

Due to developing satellite technology, extraction of man-made features such as roads and buildings has become a topic of interest

for photogrammetric and remote sensing communities. Extraction of buildings in urban areas is a complicated problem due to the

complexity of shapes, textures, and contexts. In order to dealing with this problem, an object-based image analysis is used togenerate homogeneous regions from a pixel-based image, which is called image segmentation. This article introduces a region-based

image segmentation algorithm based on k-means clustering in order to extract buildings. The algorithm contains three steps: (1)

clustering procedure, (2) image segmentation, and (3) boundary improvement. The approach is evaluated using a case study in

Tehran, Islamic Republic of Iran. Very high resolution GeoEye satellite imagery was used in the case study. Experimental results

show that the proposed algorithm extracts 76.1 % of building areas with a quality percentage 62.9 % in an urban area.

* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

1. INTRODUCTIONAutomatic extraction of man-made objects from satellite images

is a significant research task in photogrammetry and remote

sensing. With a rapid increase of satellite technology, it is

urgent to extract man-made objects, such as buildings and

roads, from satellite images. Data acquisition for mapping andGeospatial Information System (GIS) by photogrammetric

methods has traditionally been carried out in manual extraction

of cartographic features from images of the terrain surface

ranging in scale from 1:3000 to 1:90000 (Sowmya and Trinder,

2000). Although this strategy is efficient under the viewpoints

of accuracy and precession, it is generally time-consuming and

expensive, what certainly have limited the amount, resolution,

and revision cycles of terrain information that can be extracted

by using current digital photogrammetric systems. (Dal Poz and

do Vale, 2003). In this context, building extraction has

remained as an important issue of research.

Up to now, fully automatic algorithms for building extraction

seem to be far away from a plenary state and, consequently, no

such operational system is expected to be available in nearfuture. Image segmentation is the process of dividing an image

into homogenous regions, which is a neccessary step for higher

level image processing such as image analysis, pattern

recognition, and automatic image interpretation (Blaschke and

Strobl, 2001). So far, there are over 1000 types of developed

segmentation methods (Zhang, 2001). General segmentation

methods consist of global behaviour-based and local behaviour-

based methods (Kartikeyan et al., 1998). Global behaviour-

based methods group the pixels based on the analysis of the

data in the feature space. Typical examples are clustering and

histogram thresholding. Local behaviour-based methods

analyze the variation of spectral features in a small

neighbourhood. Typical examples are edge detection and

region extraction (Fu and Mui, 1981).

However, as reported in (Li et al., 2008), not all of the

segmentation methods are possible for high resolution satellite

imagery due to the following facts:

1. This imagery is multi-spectral and multi-scale, so boththe complexity and redundancy are increased

obviously.

2. The imagery provides the more details such as color,shape, context and texture.

3. Different class has its inherent features in differentscale. E.g. at coarse scales we may find fields, while

at finer scales we may find individual trees or plants.

So the segmentation model on one scale must be

modified when used on the other scale.

In this article, a fully automatic algorithm is introduced for

building extraction from high resolution multispectral GeoEye

imagery. For this purpose, we present a region-based image

segmentation procedure together with a boundary improvement

algorithm. The proposed algorithm is evaluated for a case study

in Tehran, Islamic Republic of Iran.

The remainder of this article is organized as follows: Section 2

explains the proposed algorithm in detail. Section 3 presentsexperimental results, and Section 4 concludes the work.

2. OVERVIEW OF THE ALGORITHMFigure 1 depicts a flow diagram of the proposed algorithm. At

first, clustering procedure is used in order to divide an original

image into a binary clustered image including building and non-

building layers. A k-means clustering algorithm is applied

because it is able to use all of the multi-spectral bands for

providing a single band clustered image. In the second step, we

apply a kind of region-based image segmentation which

includes k-means clustering and single thresholding, seed point

generation, and region growing. In the final step, boundary of


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extracted buildings is improved by a logical process. Next

sections describe each step in detail.

Figure 1. Flow diagram of the proposed algorithm

2.1 Clustering ProcedureIn order to divide an original image (see Figure 3.a) into a

clustered image (see Figure 3.b), a well-known clustering

procedure referred to as k-means clustering (Gray and Linde,

1982; Seber, 1984; Spath, 1985) is used. An overview of the k-

means clustering algorithm is shown in Figure 2. The

procedure is initialised by selecting the predefined number of

centeroids (k) in the multi spectral space. The selection of these

centeroids at this stage is arbitrary with exception that no two

may be the same. The location of each pixel in the image is

examined and the pixel is assigned to the nearest candidate

centeroid. This assignment would be made on the basis of the

Euclidean distance measure. Then, the new set of centeroids is

computed. This two-phase procedure is repeated until it reaches

the maximum iteration (m). Two parameters k and m must be

specified beforehand by the user. The generated clustered

image includes building and non-building layers.

Figure 2. Overview of the k-means clustering algorithm

(a)

(b)

Figure 3. Applying k-means clustering with k = 2 and m = 4 on

the original image. (a) Original image, and (b)

Clustered image

2.2 Image SegmentationSince the generated building layer contains buildings and their

adjacent similar spectral features such as roads, shadows, cars,

yards etc, firstly, we must separate all features as it is possible

and then extract building features. For this purpose, a region-

based image segmentation algorithm is used. The image

Predefined number

of clusters

Centroids determination

Compute distance to centroids for

each building pixel

Assigning each building pixel to

its nearest centroid

> Maximum

iterationNo

Yes

Original image

Clustered image


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segmentation consists of three steps. At first, k-means

clustering divides the building layer into predetermined number

of clusters (see Figure 4.a). The predetermined number is

selected on the basis of the building variation in size, shape, and

color. Each cluster contains a set of discontinuous regions. In

order to separate small regions from big ones, the histogram of

all regions is plotted in order to represent cumulative

distribution of pixels for each region (see Figure 4.b). If thenumber of pixels belonging to a region is less than a user-

defined threshold, then this region is removed. The resulting

image is shown in Figure 4.c.

(a)

(b)

(c)Figure 4. Applying k-means clustering and thresholding on the

building layer, respectively. (a) K-means clustering,

(b) Histogram for the discontinuous regions, and (c)

Thresholding (regions are shown with different

colors)

At second, seed point generation is implemented automatically

in order to provide seed points for region growing. Firstly, the

original image is converted into an 8-bit gray scale image. Then

the position of a seed point for each region is defined as

follows:

=

rgni )i,yig(x

rgni )i,yi.g(xiyySeed

rgni )i,yig(x

rgni )i,yi.g(xix=xSeed

(1)

where rgn is an evaluation region and g(xi,yi) is the gray scale

value of the ith point which belongs to the region. Both spatial

(i.e. (xi,yi)) and homogeneity (i.e. g(xi,yi)) information take part

in defining seed points. So seed points as starting points are

more suitable than centeroids for a region growing stage. The

generated image depicts regions with their seed points (see

Figure 5).

Figure 5. Seed point generation and region growing. (a)

Regions, and (b) Regions with their seed points

In the final stage, a region growing method is performed to

extract buildings. For this purpose, the process starts at each

seed point in the image with one-pixel objects, and in numerous

subsequent steps, smaller image objects are merged into bigger

ones (Carleer et al., 2005). The similarity condition is defined

by means of a logical statement if pixels in the region are

similar enough in terms of spectral variance property as follows:

extraction of buildings in urban areas from very high resolution satellite images

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