Full text: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

International Archives of Photogrammetry and Remote Sensing,Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
Annett Faber, Wolfgang Forstner 
Institute of Photogrammetry, University Bonn, NuBallee 15, D-53115 Bonn, Germany, email: (annett,wf)@ipb.uni-bonn.de 
KEY WORDS: texture representation, Laplacian pyramid, spectral decomposition, eigenvalues, urban structure. 
This paper describes research on the extraction of urban structures from aerial images or high resolution satellite scenes 
like the German MOMS02 sensor. We aim at a separation of neighboring textured regions important for describing different 
urban structures. It has been shown, that grey level segmentation alone is not sufficient to solve this problem. If we want 
to use texture additionally, the development of a suitable representation of texture images is required. We use the scale 
characteristics of the local autocovariance function, called SCAF, of the possibly multiband image function. The final result 
of the process are texture edges. 
Texture is one of the most fundamental and at the same 
time most interesting characteristics of visible surfaces in 
the human perception process. Therefore, in pattern recog 
nition the analysis of textures is very important. Numerous 
techniques for texture analysis have been proposed. They 
can be mainly categorized as (Haralick and Shapiro, 1992): 
1. Texture classification: For a given textured region, de 
cide, to which one out of a finite number of classes the 
region belongs. 
2. Texture synthesis: For a given textured region, deter 
mine a description or a model. 
3. Texture segmentation: In a given image, which con 
tains many textured regions, determine the boundaries 
between these regions. 
The procedures developed for the solution of these prob 
lems can be subdivided into three categories: structural ap 
proaches, statistical approaches and filterbased approaches 
(J.-R de Beuaville and Langlais, 1994, Reed and du Buf, 
1993, Shao and Forstner, 1994). 
Structural approaches assume that textures contain de 
tectable primitive elements which generate the texture 
in a regular or irregular manner, following certain con 
nection rules. Such approaches use e. g. so-called 
texture grammars (Carlucci, 1976) or texture elements 
(Julez and Bergen, 1983). 
Due to the almost unlimited number of possible unit 
patterns and the complexity of the rules, these proce 
dures have shown lower success in texture analysis 
than the following two approaches. 
Statistical approaches use statistical characteristics, deriv 
able from the images. These characteristics are often 
sufficient for texture classification and segmentation, 
without needing generation rules for the textures. They 
can further be separated into approaches which use 
• descriptions derivable directly from the images 
such as variance, entropy or other values ob 
tained from the local pixel neighborhood, as e. g. 
co-occurrences (Haralick, 1979) and 
• model based descriptions, such as autoregres 
sive models, Markov or Gibbs random fields (An- 
drey and Tarroux, 1996, Derin and Cole, 1986). 
Statistical approaches usually refer to the image grid, 
thus have difficulties in handling scale space proper 
Filter based approaches assume that the image function 
can be described locally by its amplitude spectra. Ga 
bor wavelets have been the first choice together with 
linear and nonlinear post processing steps to achieve 
multi scale features in the different channels (A. C. Bovik 
and Geisler, 1990, Bigun and du Buf, 1992, Malik and 
Perona, 1990). This class of approaches is motivated 
by their similarity to the human visual system. 
The advantage is that the filter responses for basic ge 
ometrical transformations are predictable and the fil 
ters work equally for natural scenes of different texture 
types. However, there is no general approach for the 
selection of a suitable filter bank and for the linkage of 
different image channels. 
We are interested in analyzing satellite and aerial images 
especially for extracting urban structures. Therefore, we 
need powerful techniques for image segmentation in the 
presence of natural textures. Grey level or color segmen 
tation results often suffer from over or under segmentation. 
Texture segmentation can reduce the amount of over seg 
mentation, mostly without the risk of under-segmentation. 
Our first experiences with Gabor wavelets based on (Ma 
lik and Perona, 1990) demonstrated the feasibility of a fil 
ter based approach for texture edge extraction (Shao and 
Forstner, 1994). Our approach used the edge detection 
scheme in ourfeature extraction program FEX (Fuchs, 1998), 
cf. Figure 1c). It exploits its ability to handle multichannel 
images by taking the filter responses as a multichannel im 
age as input. 
Due to the heavy algorithmic complexity, we develop a new 
filter based scheme for deriving texture edges again us 
ing the edge extraction scheme of our feature extraction

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.