In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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interpretation of the decorrelation due to too many possible 
human activities. 
The paper is organized as follows. Firstly, we point out main 
ideas of the CCD technique. Then we describe our study area 
together with the data, as well as our method for analysis of 
CCD results in order to obtain information about potential 
human-induced scene changes, such as building up areas of 
hard surface on a terrain that was previously a part of a desert. 
In order to cover a wide range of applications and situations, the 
proposed method is intentionally designed so that it does not 
need any specific knowledge source about the terrain. Still, 
once CCD results are obtained, various knowledge sources can 
be taken into account in order to improve the final 
interpretation. These knowledge sources can be related to the 
sensors (operational principles and experience) or to the 
situation at hand (context - terrain type, land-use, historical 
background etc.). A way to include them in the reasoning 
process is discussed here too. In addition, in order to further 
improve the quality of the obtained output, usefulness of 
applying a spatial regularization technique is tested as well. 
Finally, we validate the obtained results and propose ways to 
continue the work. 
2. ON COHERENT CHANGE DETECTION 
Starting points for CCD processing are the complex images of 
an interferometric SAR image pair (two images from 
approximately the same geometry collected at two different 
times). Namely, CCD uses the correlation between them and 
provides information on the stability of the target (Matikainen, 
Hyyppa and Engdahl, 2006; Price and Stacy, 2006). For 
instance, forests have a low coherence value, which means that 
such a type of target has changed much from one image to 
another (thus in time corresponding to the collection of these 
two images), while urban areas typically have high coherence 
values even between image pairs separated by several years 
(Luckman and Grey, 2003; Matikainen, Hyyppa and Engdahl, 
2006). 
The coherence can be expressed as the product of five dominant 
contributions, as shown in (Zebker and Villasenor, 1992; 
Preiss, Gray and Stacy, 2006): 
1) the relative backscatter signal to radar receiver noise ratio in 
the interferometric image pair, 
2) the volume decorrelation, 
3) the baseline decorrelation, 
4) the decorrelation related to mismatch between the coherent 
acquisition apertures and image-formation processing stages 
used to produce the primary and repeat-pass imagery, and 
5) the decorrelation in the scene over the repeat-pass time 
interval (temporal decorrelation); this type of decorrelation is 
the one we are interested in, as it is determined by various 
sources of scene change, such as environmental effects 
(moisture changes, atmospheric effects) or man-made 
disturbances. 
The value of the product of the first four contributions 
mentioned above is close to one if the repeat-pass imaging 
geometry is designed carefully and if interferometric processing 
steps are performed, such as compensation for aperture and 
processor mismatch as well as image registration (Preiss, Gray 
and Stacy, 2006). Therefore, under such conditions, the 
coherence of the scene image reflects the true scene coherence 
over the repeat-pass interval. For the data used in this paper, 
these conditions are fulfilled. 
Therefore, a starting point in our analysis is the fact that in this 
type of scene/terrain, low coherence values can refer to the 
moving ground (sand), which is perturbed all the time, while 
high coherence values can be related to a hard surface (e.g. 
concrete). 
3. STUDY AREA AND THE DATA USED 
The study area is an airfield that is located in a desert part of 
Israel. This type of terrain is a good start for analysing the 
usefulness of the CCD method for detecting potential human 
activities due to low soil moisture as well as low vegetation. 
As far as the data are concerned, four ALOS PALSAR (Phased 
Array type L-band SAR) images (Rosenqvist, Shimada and 
Watanabe, 2004) of the scene in the descending mode are 
processed, corresponding to four different dates of acquisition: 
15 November 2007, 15 February 2008, 1 April 2008 and 17 
May 2008. Starting from the Single Look Complex data, three 
CCD results are obtained using the following pairs: 15 
November 2007 and 15 February 2008 (period 1), 15 February 
2008 and 1 April 2008 (period 2), as well as 1 April 2008 and 
17 May 2008 (period 3). As an example of the three obtained 
CCD results, Fig. 1 contains the CCD result for the images of 
period 1. The images of the other two periods can be found in 
(Milisavljevic, Closson and Bloch, 2010). The coherence 
images are generated using the module Insar of Erdas Imagine 
2010 platform. 
Figure 1. CCD result for the images acquired on 15 November 
2007 and 15 February 2008 (period 1) 
Figure 2. Amplitude of the SAR data acquired on 15 November 
2007 
In order to illustrate the usefulness of the SAR phase 
information used to obtain the CCD results, Figures 2 and 3