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 
     
COMBINATION OF GENETIC ALGORITHM AND DEMPSTER-SHAFER THEORY OF 
EVIDENCE FOR LAND COVER CLASSIFICATION USING INTEGRATION OF SAR 
AND OPTICAL SATELLITE IMAGERY 
H. T. Chu' and L. Ge 
School of Surveying and Spatial Information Systems, The University of New South Wales, 
Sydney NSW 2052, AUSTRALIA 
Ph. +61 2 93854174, Fax. *61 2 9313 7493 
ht.chu@student.unsw.edu.au 
Commission VII, Working Group VII/4 
KEY WORDS: Accuracy, Classification, Feature, Integration, Land Cover, Landsat, SAR, Texture 
ABSTRACT: 
The integration of different kinds of remotely sensed data, in particular Synthetic Aperture Radar (SAR) and optical satellite 
imagery, is considered a promising approach for land cover classification because of the complimentary properties of each data 
source. However, the challenges are: how to fully exploit the capabilities of these multiple data sources, which combined datasets 
should be used and which data processing and classification techniques are most appropriate in order to achieve the best results. 
In this paper an approach, in which synergistic use of a feature selection (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. Multi-date SAR data, including ALOS/PALSAR, ENVISAT/ASAR and optical (Landsat 5 TM+) 
images, were used for this study. Textural information were also derived and integrated with the original images. Various combined 
datasets were generated for classification. Three classifiers, namely Artificial Neural Network (ANN), Support Vector Machines 
(SVMs) and Self-Organizing Map (SOM) were employed. Firstly, feature selection using GA was applied for each classifier and 
dataset to determine the optimal input features and parameters. Then the results of three classifiers on particular datasets were 
combined using the Dempster-Shafer theory of Evidence. Results of this study demonstrate the advantages of the proposed method 
for land cover mapping using complex datasets. It is revealed that the use of GA in conjunction with the Dempster-Shafer Theory of 
Evidence can significantly improve the classification accuracy. Furthermore, integration of SAR and optical data often outperform 
single-type datasets. 
1. INTRODUCTION 
1.1 Integration of optical and SAR 
Synergistic uses of different kind of remote sensing data, 
particularly, multispectral and SAR imagery for land cover 
classification has become an attractive research area since 
advantages of each kind of data sources can be integrated 
together in order to enhance the classification performance. 
Many studies based on the combination approach using 
different datasets and classification techniques have been 
conducted (e.g. Erasmi and Twelve 2009, Sheoran et al. 2009, 
Chu and Ge 2010, Ruiz et al. 2010). Most authors reported that 
the integration of multiple types of data has led to improvement 
in classification performance. 
Although use of multiple types of remote sensing data has high 
potential to increase the classification accuracy it also makes 
data volume increase rapidly with large amount of highly 
correlated features and redundant information. Unfortunately, 
employing large data volume does not always result in an 
increase in classification accuracy. In contrary, it will also 
increase uncertainty within dataset and could reduce 
classification accuracy significantly. According to Kavzoglu 
and Mather (2003) a large amount of data inputs decreases 
generalisation capabilities of the classifiers and produce more 
redundant and irrelevant data. Lu and Weng (2007) also pointed 
  
* Corresponding author. 
out that utilisation of too much input data may not improve (but 
can actually decrease) the classification accuracy, and it is 
important to select only input variables that are useful for 
discriminating land cover classes. Hence, the challenging task is 
how to select optimally combined datasets which give the best 
classification. 
The Feature Selection (FS) techniques often employed to search 
for optimal or nearly optimal input datasets. Many FS methods 
have been used in remote sensing such as exhaustive search, 
forward and backward sequential feature selection, simulated 
annealing and Genetic Algorithm (GA). Numerous studies have 
shown that the GA technique is very efficient in dealing with 
large datasets and has a larger chance to avoid a local optimal 
solution than other methods (Huang et al. 2006, Zhou et al. 
2010). Another advantage of the GA techniques is its capability 
to search for input features and parameters of classifier 
simultaneously. 
1.2. Classification techniques 
Applying appropriate classification algorithms is also very 
important for land cover classification. The traditional 
parametric classification algorithms such as Minimum Distance, 
Maximum Likelihood (ML) classifiers have been used widely to 
classify remote sensing images. These classifiers can produce 
relatively good classification results in rather short time.