Full text: Proceedings, XXth congress (Part 7)

  
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
Segmentation of image and extraction of the homogeneous 
areas as forest types can be develop the classification results. 
Hence, the main aim of this study was to compare of the pixel- 
based classification method with object oriented classification 
techniques on ETM- data for forest type Mapping in mixed 
hardwood forests in the north of Iran. Among the object- 
oriented techniques, recognition of which method can 
accurately extract forest types on ETM+ data was another aim 
in this study. 
Beside these, in the some studies it was resulted that adding 
artificial processed Bands facilitate improving the result 
classification. The Ratio transforms are often used in image 
processing to reduce radiometric effects of slope, sunlight 
angles or seasonal variability (Eva Ivits & Barbara Koch, 2001). 
The first three components contain more information contrary to 
each band individually. The brightness and greenness axes of 
the Tasseled Cap calculation can be useful in topographic 
variation and to differentiate between closed forest canopy 
conditions (Cohen and Spies, 1992). Then, in the study it was 
also investigated that these bands can be improve the results in 
the both classification methods. 
2 Object oriented method 
In general, the object-oriented approach and the image analysis 
process can be divided into the two principal workflow steps, 
segmentation and classification. 
2.1 Segmentation 
In a principal definition, “objects” or “local pixel group” 
information is basis for the object oriented classification 
approaches. Segmentation principally means the grouping of 
picture elements by certain criteria of homogeneity 
(Hildebrandt, 1996). In the object-oriented approach, an image 
is subdivided into homogenous objects by segmentation 
analysis. Therefore image will be heterogeneous and contrast of 
objects will increase (see figure 1). 
Segmentation can be done in multiple resolutions in different 
levels of objects by criteria such as scale, color and form. 
1B 
  
Figure 1: small windows of study area 453 (RGB) before 
segmentation (1A) and after segmentation (1B) show contrast of 
objects in a forest area. 
2.2 object oriented classification techniques 
The meaningful primitive objects, which obtained by 
segmentation, can be classified through two methods: Sample- 
based classification by nearest neighbor classifier and 
Rule-based classification by membership function technique. 
The experiences were showed that when several different 
feature order objects into classes, the nearest neighbor method 
should be used and when only few discrete features can separate 
classes from each other, use of membership function is an 
optimal choice (Ivits. E. & Koch. B. 2000). 
The nearest neighbor classifier, as a supervised classification 
method needs training area in a multidimensional feature space. 
It would be useful when user has no knowledge to describe 
feature spaces. In the nearest neighbor method or the sample- 
based method, the primitive objects are classified through 
similarity to training units or segments for each class. The rest 
of objects in the image are belonged to their nearest sample in 
each class. It usually uses spectral information of bands as 
feature space for description of classes that are to be classified. 
After segmentation, in addition to spectral attributes, the objects 
will have extra information such as shape, texture, context 
attributes and topological relations between neighborhoods and 
other objects. This information can be used for exact extraction 
of each class in classification. 
In the Membership Function (Rule-based) Method, segments 
are classified by membership functions, which are based on 
fuzzy sets of object features. Fuzzy logic is a mathematical 
approach to quantify uncertain statements (willhauck, 2000). To 
be aware of relevant information which can correctly classify 
classes play important role in this method as a knowledge-based 
system. In this method, the interpreters can define thresholds to 
be belonging objects to each class by suitable attribute trough 
fuzzy sets. 
3. METHODOLOGY 
3.1 Study area 
The study area is located at educational and research forest, 
Faculty of Natural Resources of Tehran University in the north 
of Iran between 51?33'12"E and 51?39'56" E longitude and 
36°32°08” N and 36°36°45 5” N latitude. The whole forest has 
been subdivided to seven Districts. The study has been 
performed on three districts (Patom, Namkhaneh and Gorazbon 
respectively), are about 3000 hectares (see figure 2). Altitude 
ranges from 50 m to 1350 m (sea level). Because of different 
aspects and range of altitude, a Variety of forest types have been 
established. 
  
  
  
& 
"esae 
em 
E. 57) 
.. Legend 
  
  
Parcels 
| 
« |I|/\/Districtions 
3 | B] Study area 
[ 
| 
X 
{ ; 
A bs 
\ 
t 1 Scale 
tl 1:80000 
  
  
  
  
  
Figure 2: location of study area in the research forest of Tehran 
University (Kheyrodkenar) in the north of Iran. 
3.2 Data 
In order to investigate ETM-- data potential for forest types 
mapping, a small window on 164-35 Scene from 2nd August 
2000 was selected. Except for thermal bands, all multi-spectral 
and panchromatic data were used for this study. 
[n addition, some ancillary data extracted DEM such as aspect 
and elevation maps were resized to spatial resolution of satellite 
1106 
pr 
Tei 
rej 
ref 
Th 
to 
trui 
trat 
met
	        
Waiting...

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.