Full text: Mapping without the sun

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08/01/2004, (g) cubic interpolated image, and (h) SR 
reconstructed image. 
The proposed algorithm was tested using real MODIS band-4 
images which were captured on 28, 30 December 2003 and 1, 4, 
6, 8 January 2004 respectively. We obtained these images from 
the Satellite Remote Sensing Receiving Station of Wuhan 
University. We chose two series of 50x50 regions to test the 
algorithm independently. The multi-temporal block images of 
the two regions are respectively shown in Figures 2(a)-(f) and 
Figures 3(a)-(f). 
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Figure 3. (a)-(f) Images respectively captured on 28/12/2003, 
30/12/2003, 01/01/2004, 04/01/2004, 06/01/2004 and 
08/01/2004, (g) cubic interpolated image, and (h) SR 
reconstructed image. 
In the experiments, the corresponding parameters were set 
as. fi ~ \ , X = 0.008 and 7V = 10. A 3x3 Gaussian blur 
kernel with unit variance was assumed and was commonly 
employed. We assume the down-sampling factors in both the 
horizontal and vertical directions have a value of 2. The cubic 
interpolated images which are shown in Figure 2(g) and Figure 
3(g) were regarded as the initial guesses of the SR images. The 
corresponding SR reconstructed results are respectively shown 
in Figure 3(h) and Figure 4(h). By visual comparison, it is seen 
that the results of the proposed SR algorithm are much clearer 
than those of the cubic interpolation algorithms, the reason for 
which is that these results fused the complementary information 
in different observed images. 
In this paper, we have proposed a SR image reconstruction 
algorithm to multi-temporal remote sensing images. Employing 
the joint MAP framework, the proposed algorithm can 
simultaneously estimate the image registration parameters and 
the HR image. We tested this algorithm using multi-temporal 
MODIS images. Experiment results validated the proposed 
algorithm performs better than single cubic interpolation 
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