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Mapping without the sun
Zhang, Jixian

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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).
(e) (0
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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