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 
  
(c) 
   
Figure 5. Change detection maps (a) RKL change maps (b) RDR change maps (c) DSMT fusion results. 
Figure 6 illustrates the temporal classification of the change 
detection maps obtained by using the K-means algorithm. As 
we can see the proposed approach puts in evidence all the 
change classes’ categories which have been simulated. 
  
(b) 
   
(d) 
Legend: HE Stable areas LI] Abrupt changes 
  
O Periodic behaviour Evolutionary behaviour. 
Figure 6. Temporal Classification results. (a) RDR 
temporal classification results, OA=0.67, (b) RKL 
temporal classification results, OA =0.61, (c) DSMT 
fusion temporal classification results, OA-0.72, (d) 
Temporal ground truth image. 
6.2 Case of real data 
Then, the proposed approach is applied on a set of 14 
ENVISAT SAR images covering the region of Tunis City (cf. 
Figure 7) which is a developing region touched by several kinds 
of changes. As we can notice, The 14 SAR images cover a large 
temporal period. 
Figure 8 corresponds to the obtained and filtered reference 
image. As we can see, it matches the most stable areas 
according to original images (cf. figure 7). We can also notice 
that the NL-means filtering preserve the SAR information. 
Figure 9 shows some DSMT based change detection maps 
generated throughout the proposed approach. As we can see 
some changed regions are highlighted by the detection process. 
These maps are then used to generate the temporal classification 
map which is illustrated by figure 10. 
As can be noticed from this figure, most of the stable areas are 
correctly delimited. The black region at the right down side 
which has a periodic behaviour (see figure 7) is also rightly 
classified. 
   
Figure 8. The resulted non filtered and filtered Reference image 
for real data.