The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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Figure 1. Left: A cropland object in Weiterstadt (Germany) in 
an orthorectified RGB IKONOS image with a 
resolution of 1 m (acquired 24/06/2003). Right: the 
edge image as a first step of the verification algorithm. 
It is the goal of this paper to present such a segmentation 
algorithm and first examples for how it can be used to improve 
the overall verification process. We start with a description of 
the segmentation algorithm. After that, the way the 
segmentation algorithm can be embedded into the verification 
process will be presented. This is followed by preliminary 
results achieved for images of different resolution and from 
different locations, which will be the basis for a discussion of 
the possibilities and the limitations of the segmentation 
algorithm for this specific application. The paper concludes 
with a summary and an outlook. 
(2007) used mean colour difference, edge strength of the shared 
borders and colour standard deviation to merge segments of 
road objects in an iterative way after generating an over 
segmented image using the Normalized Cut algorithm. Their 
algorithm requires a priori knowledge given by a GIS and the 
setting of several thresholds. 
2.1 General Segmentation Approach 
Let a multispectral image of N bands be represented by the grey 
level vectors g(x, y) = [g,(x, y), g 2 (x, y), .... g N (x, y)] T at 
position (x, y). It is the goal of region-based segmentation to 
partition that image into disjunct regions /?, of homogeneous 
grey level vectors and to determine the closed boundary 
polygons of these regions. Whereas in theory the boundaries 
separating these regions are infinitely thin, the reality of the 
imaging process will blur these boundaries, so that they have 
actually a certain extent in image space. Typically the region 
boundaries correspond to edges in the image that can be 
approximated by polygons. Forstner (1994) represents an image 
as the union of segment regions R h line regions L„ and point 
regions P h based on a classification of each pixel of the images 
as being either homogeneous, linear, or point-like. In Fuchs 
(1998), the symbolic representation was expanded by the 
neighbourhood relations of these regions to define a Feature 
Adjacency Graph (FAG). In order to distinguish homogeneous 
pixels from other pixels, a measure for homogeneity H can be 
used that is based on an analysis of the first derivatives of the 
grey values in a local neighbourhood (Forstner, 1994): 
2. REGION-BASED SEGMENTATION 
The segmentation of objects provides the basis for the 
interpretation of images for humans as well as for the fields of 
Image Analysis and Computer Vision. Compared to the human 
ability to segment objects directly from an image without great 
effort, the automatic extraction of objects in the field of image 
analyzing is difficult due to problems such as variable lighting 
conditions, poor contrast and the presence of noise. Whereas 
many segmentation approaches have been presented in the past 
(e.g. Gonzalez and Woods, 2002; Forstner, 1994), there is no 
generally accepted optimal approach for segmentation, 
especially if homogeneous regions are to be extracted. One the 
one hand, the extracted segments should represent the digital 
image as precisely as possible, even showing relatively small 
detectable features; on the other hand, a certain generalisation is 
required in order to reduce the impact of noise on the 
segmentation results. Furthermore, segmentation should only be 
based on a small number of control parameters that should be 
easily interpretable. 
The algorithm presented in this section starts with a Watershed 
segmentation (Gonzalez and Woods, 2002) that achieves a 
strong over-segmentation of the image. After that, neighbouring 
segments are merged on the basis of a statistical analysis of the 
properties of the initial segments and their shared boundary. 
The merging process should only require the setting of few 
control parameters and no training. In that regard it differs from 
existing grouping algorithms. For instance, Luo and Guo (2003) 
introduced a general grouping algorithm based on Markov 
random fields, using single segment properties such as area, 
convexity and colour variances, and pair-wise properties such 
as colour differences and edge strength along the shared 
boundary. The algorithm requires a training phase. Grote et al. 
? 
In Equation 1, g ix and g iy are the first derivatives of the 
grey levels g, of band i by x and y, respectively. G is a 
Gaussian smoothing filter with scale parameter , and 'J is 
the variance of the smoothed grey level differences G * g^ 
and G * g iy , which can be derived from an estimate of the 
noise variance of band i (Briigelmann and Forstner, 1992) 
by error propagation. The sum is to be taken over the N bands 
of the digital image. The scale parameter defines the size of 
the local neighbourhood that is taken into account. By 
normalising the smoothed grey level differences by their 
standard deviations, the selection of a threshold H max for H to 
distinguish homogeneous pixels from others can be reduced to 
the selection of a significance level for a statistical test 
(Forstner, 1994). 
The image regions /?, could be determined as connected 
components of homogeneous pixels, thus of pixels whose 
homogeneity measure H is smaller than H max . However, small 
gaps within extracted line regions that occur due to poor local 
contrast often cause a spilling effect, i.e., the erroneous merging 
of regions that represent different object parts. Furthermore, it is 
not straightforward to obtain meaningful closed boundary 
polygons of the homogeneous segments, gives a typical result. 
The main edges of the image are represented well, although the 
edges information appears to be captured incompletely. The 
segments in the label image do not represent the image structure 
well due to small gaps in their boundaries. A considerable 
portion of the image is not assigned to any label.