Full text: Proceedings, XXth congress (Part 4)

  
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
Segmentation (MS). The segmentation algorithm is a bottom-up 
region-merging technique. MS starts by considering each pixel 
as a separate object. Subsequently, adjacent pairs of image 
objects are merged to form bigger segments. The merging 
decision is based on local homogeneity criterion, describing the 
similarity between adjacent image objects. The pair of image 
objects with the smallest increase in the defined criterion is 
merged. The process terminates when the smallest increase of 
homogeneity exceeds a user-defined threshold. Therefore a 
higher threshold will allow more merging and consequently 
bigger objects, and vice versa. The homogeneity criterion is a 
combination of colour (spectral values) and shape properties (a 
combination of smoothness and compactness) Applying 
different thresholds and colour/shape combinations, the user is 
able to create a hierarchical network of image objects which is 
necessary to extract different types of objects (area and linear 
objects) because they require varied segmentation parameters 
(eCognition User Guide, 2004). Darwish et al. (2003) report 
about a research work about finding optimum segmentation 
parameters. Up till now, finding the optimum segmentation 
parameters is a trial and error process which is usually carried 
out by experienced analysts. 
As already mentioned MS is a region-merging algorithm which 
depends strongly on the heterogeneity of the image data and on 
local contrasts. Therefore the image content may influence the 
transferability of segmentation parameters. The segmentation 
result of transferred segmentation parameters to another image 
of the same sensor in a similar geographic region can be 
evaluated visually or by evaluating the classification result 
which is based on the segmentation. A comparison of average 
image object sizes between different images could also serve as 
an indicator for the transferability of segmentation parameters. 
2.2 Knowledge-base 
Following image segmentation, the next step is describing the 
output classes. This is achieved using a knowledge-base (Class 
hierarchy) which defines the properties of the classes to be 
extracted. Each class description is composed of fuzzy 
expressions (Membership functions) that include logical 
operations and hierarchical class descriptions. These 
descriptions may include not only spectral properties but also 
shape and size characteristics, context, and texture information. 
Within this class hierarchy it is possible to inherit image object 
properties from a super-class to a sub-class and also to group 
classes semantically. 
3. TEST SITES 
3.1 Image Data 
In this study, images from the Indian Remote Sensing Satellite 
(IRS) are used. The three multispectral bands green, red, and 
Near infrared (NIR) were merged with the panchromatic band 
to produce a pan-sharpened image with a pixel size of 5m. The 
images are not radiometrically corrected. The test sites belong 
to two geographic regions in Algeria. The first is the coastal 
region (Northern part of the country) and the second is the 
desert region (four test sites in each region). The image contents 
comprise built-up areas, agricultural land, different kinds of 
vegetation, desert areas, irrigated fields, several water bodies 
(sea, lake, river), and lines of communication like roads. The 
distance between the most western and most eastern two test 
sites in the coast region is approximately 600km. Table 1 and 2 
list the used images with their dates of acquisitions and extents, 
The dates of acquisition spread over nine months (coast) and 
two years (desert). However it has to be noted that the images in 
the desert region were all taken in December. 
Table 1: Test Sites Coast 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Mosta- 
Test Site Algiers iie As : 
ganem giers Jijel Mandoura 
Date of 
acquisi- 25.11.1999 30.1.2000 11.3.2000 14.8.2000 
tion 
Extent x10 38 x 19 17 x 10 7x6 
[km x km] 
Table 2: Test Sites Desert 
ad Northeast 
4 Hassi-El- : eS 
Test Site ; of Ouargla | N'Goussa 
Frid 
Ouargla 
Date of 
acquisi- 25.12.1998 6.12.1998 19.12.2000 19.12.2000 
tion 
Extent 7x10 12x15 RYE 8x5 
[km x km] 
  
  
  
  
  
  
3.2 Output Classes and Evaluation 
The output classes comprise four base classes (water, built-up, 
non built up, and roads). There is no ground truth nor adequate 
reference data available for Algeria. Therefore visual 
interpretations of the images are used as reference data. 
The evaluation of the classification is done using two 
assessment tools, Error Matrix (EM) and Kappa statistic values 
(K). While the former reports three values: Producers Accuracy 
(PA), Users Accuracy (UA) and Overall Accuracy (OA), the 
later reports a single K value for each class and an Overall 
Kappa value (OK). More detailed information about accuracy 
assessment from remotely sensed images can be found in 
Congalton and Green (1999). 
4. PRACTICAL INVESTIGATIONS 
Practical investigations in this research have been carried out in 
two steps. The objective of the first (transferability test) Is to 
come up with a number of potential classification rules for 
specific feature classes and test their transferability to other 
images of each geographic region. The objective of the second 
step (base rule set) is to analyse the above mentioned 
classification rules to define a set of rules that is stable and 
transferable and achieves the highest possible classification 
accuracies. 
4.1 Transferability test 
After the definition of segmentation parameters, classification 
rules were set-up for each test site. Following the classification 
of each image accuracy assessment was performed. Within each 
of the geographic regions, the sets of classification rules of each 
image were transferred to the other three images. The 
classification results were evaluated after applying the 
transferred sets and also after adapting (adjusting existing rules 
-changing image object property values- and if necessary add 
new rules or feature classes specific for an image) the 
transferred sets to the new image. 
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