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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 
MULTI-TEMPORAL SAR CHANGE DETECTION AND MONITORING 
S. Hachicha, F. Chaabane 
Carthage University, Sup'Com, COSIM laboratory, Route de Raoued, 3.5 Km, Elghazala Tunisia. 
ferdaous.chaabene@supcom.rnu.tn 
KEY WORDS: Spatio-temporal NL-means filtering, Rayleigh Kullback Leibler, Rayleigh Distribution Ratio, DSMT fusion, 
temporal classification. 
ABSTRACT: 
Multitemporal SAR images are a very useful source of information for a large amount of applications, especially for change 
detection and monitoring. In this paper, a new SAR change detection and monitoring approach is proposed through the analysis of a 
time series of SAR images covering the same region. The first step of the method is the SAR filtering preprocessing step using an 
extension of the spatial NL-means filter to the temporal domain. Then, the Rayleigh Kullback Leibler and the Rayleigh Distribution 
Ratio measures are combined to detect the changes between a reference image and each SAR image of the time series at both local 
and global scale. These measures are combined using the Dezert-Smarandache theory which takes into account conflicts between 
sources and thus enhances the dual change detection results. Finally, a pixel based temporal classification is applied starting from the 
obtained change maps in order to describe the temporal behaviour of the covered regions. 
1. INTRODUCTION 
Remote sensing applications have known a fast expansion 
thanks to the diversity and the amount of satellite images. This 
paper focuses on the problem of change detection and 
monitoring. SAR data change detection allows the analysis of 
land phenomenon for a large range of applications such as the 
urban and agriculture regions monitoring, the mapping of 
damages following a natural disaster, etc. These changes can be 
of different types, origins and durations. 
In the literature, several approaches have been proposed for 
SAR change detection between two images. These methods can 
be classified in two classes: approaches based on pixel intensity 
and approaches based on local statistics. The first set of 
approaches is based on the pixel intensity and depends on the 
neighbouring pixel intensity inside the analysis window. They 
include image differencing, log-ratio measures [5], Rayleigh 
Distribution Ratio (RDR) [3], etc. For the second set of 
techniques, the local statistics are estimated by considering 
some reasonable distributions which, in practice, have been 
found particularly convenient in the case of SAR scenes. 
Indeed, the first class of measures is limited to the comparison 
of first order statistics. More information may be extracted from 
the comparison of the local probability density functions 
(pdfs).Once the pdfs parameters are estimated, their comparison 
can also be performed using different criteria and the most 
usual one is the Kullback-Leibler divergence [4]. 
All these techniques are designed for comparison between only 
two SAR images. Not many works were interested to the 
change detection and monitoring over a time series of SAR 
images [8]. Most of them are feature and model based 
Techniques, pixel based clustering techniques or frequent 
sequential pattern based techniques [6]. 
In this paper, we are interested in SAR change detection and 
monitoring through the analysis of a time series of SAR images 
covering the same region [10]. The aim of this work is to 
consider the radiometric information and to characterize the 
change by its temporal signature and evolution. Then the 
investigation concerns the identification of the changes and 
     
their monitoring using a temporal classification. We believe that 
changes can be categorized in four classes: 
- Abrupt changes such as urban constructions, deforestation 
area, etc, 
- Evolutionary changes such as vegetation areas, 
- No changes areas i.e. stable areas as urban areas for example, 
- Periodic changes such as the vegetation evolution according to 
seasons. 
Thus, dual SAR change detection indicators are going to be 
used in order to classify the changes according to their temporal 
behavior. 
The main contribution of this work consists in the introduction 
of a judicious combination of two families SAR change 
detection indicators in the change monitoring approach which 
can be considered as an improvement of the work done in [10]. 
Indeed, the proposed technique is based on a combination 
between a local statistics and a pixel intensity change detection 
indicators. It suggests an original strategy for changes temporal 
classification using the behaviour of the dual change measures 
between each SAR image and a constructed reference image. 
The proposed multi-temporal change detection and monitoring 
approach follows these main steps (cf. figurel): 
a) As a preprocessing step, we apply a spatio-temporal adaptive 
filtering of the SAR sequence to reduce the speckle noise and 
estimate the reflectivity [10]. 
b) Construction of the reference image which represents an 
intermediate state throughout the SAR time series. 
c) Identification of the changes between the generated reference 
image and each filtered SAR image using a local statistics 
change detection family operator which assumes a Rayleigh 
distribution for a non local neighbourhood [10]. The 
contribution of this paper is to introduce a pixel intensity 
change detection indicator applied on non filtered images. Thus, 
the local statistics and a pixel intensity change detection 
measures are combined using the DSM theory [11] in order to 
detect temporal changes. Both local and global variations are 
then detected by the resulted detector.