However, the major limitation of these classifiers relied on its 
statistic assumptions which are not sufficiently model remote 
sensing data (Waske and Benediksson, 2007). This nature cause 
remarkable difficulties for parametric classifiers to incorporate 
different kinds of data for classification. Unlike conventional 
classifiers, the non-parametric classification algorithms such as 
Artificial Neural Network (ANN) or Support Vector Machine 
(SVM) are not constrained on the assumption of normal 
distribution and are therefore considered more appropriate for 
handling complex datasets. 
1.3. Classifier combinations 
One of the recent technical development for mapping land cover 
features is classifier combination or Multiple Classifier System 
(MCS). Each kind of classification algorithm has its own merits 
and limitations. The classifier combination techniques can take 
advantages of each classifier and improve the overall accuracy. 
Application of multiple classifier system (MCS) in remote 
sensing has been discussed in Benediksson et al. (2007). There 
are many methods for combine classifier such as Majority 
Voting, Weigh Sum, Bagging or Boosting. Du et al. (2009) 
used different combination approach including parallel and 
hierarchical classifier systems, training samples manipulation 
with Bagging and Boosting techniques for classifying 
hyperspectral data. Foody et al. (2007) integrated five classifiers 
based on majority voting rule for mapping fenland East Anglia, 
UK. Salah et al. (2010) employed the Fuzzy Majority Voting 
techniques to combine classification results of three classifiers 
over four different study areas using lidar and aerial images. 
The other technique which has been applied successfully for 
classifier’s combination is Dempster-Shafer (DS) theory (Du et 
al. 2009, Trinder and Salah 2010). 
Although both FS and the MCS techniques have been used 
widely for classify remote sensing data this study is probably 
the first effort to integrate these techniques for classifying multi- 
source satellite imagery. 
In this study, an approach, in which synergistic use of a FS 
methods with Genetic Algorithm (GA) and multiple classifiers 
combination based on Dempster-Shafer Theory of Evidence, is 
proposed and evaluated for classifying land cover features in 
New South Wales, Australia. We called this approach FS-GA- 
DS model. 
2. STUDY AREA AND USED DATA 
The study area was located in Appin, New South Wales, 
Australia, centred around the coordinate 150° 44° 30” E; 34° 
12? 30" S. The site is characterised with diversity of covered 
features such as native dense forest, grazing land, urban & rural 
residential areas, facilities and water surfaces. 
Remote sensing data used for this study includes: 
Optical: Three Landsat 5 TM-- images acquired on 25/03/2010, 
10/9/2010 and 31/12/2010 with 7 spectral bands and the spatial 
resolution is 30m. In this study 6 spectral bands except the 
thermal band were used. 
Synthetic Aperture Radar (SAR): 6 ENVISAT/ASAR VV 
polarization and 6 ALOS/PALSAR HH polarization images 
acquired in 2010 (Table 1). 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
  
  
  
  
  
  
  
  
  
  
  
  
  
Satellite/Sensors Date | Polarization Mode 
04/01/2010 HH Ascending 
22/05/2010 HH Ascending 
ALOS/PALSAR | 07/07/2010 HH Ascending 
22/08/2010 HH Ascending 
07/10/2010 HH Ascending 
22/11/2010 HH Ascending 
03/04/2010 VV Descending 
24/06/2010 VV Ascending 
ENVISAT/ASAR | 25/06/2010 VV Descending 
27/06/2010 VV Ascending 
28/06/2010 VV Descending 
25/09/2010 VV Descending 
  
  
  
  
  
  
Table 1. ENVISAT/ASAR and ALOS/PALSAR images for the 
study area. 
15042'E 
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Figure 1. ENVISAT/ASAR VV polarized image acquired on 
25/09/2010 
150°40E 150°47E 150°44E 150°46'E 150"48'E 150°50°E 
34°8'8 
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Figure 2. ALOS/PALSAR HH polarized image acquired on 
07/10/2010