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Title
Mapping without the sun
Author
Zhang, Jixian

218
SPOT5
0.9258
SAR
0.8508
Road
SPOT5
0.9141
0.881

SAR
0.7608
Water
SPOT5
0.7863
0.7729
Table 1 the SS and test result for each ground object type
As shown in the Table 1, the fusion image of airborne SAR and
multi-spectral SPOT5 has a better-recognized capability for the
types of trees, land use, and road, with an obvious
improvement in the fusion image than the original images. On
the contrary, for the types of water and buildings, there is no
better enhancement effect than the original images. And
meanwhile, the CFQ value is close to 1, implying that the
fusion image has got a good effect synthetically, adapt to
recognize many types of ground objects for human visual
system.
Tupin, F., Roup, M., 2003.Detection of building outlines based
on the fusion of SAR and optical features. ISPRS Journal of
Photogrammetry and Remote Sensing, 58, pp.71-83.
Ulaby, F T., Moore, R K., Fung, AK., 1987. Microwave
Remote Sensing. Beijing: Science press , 100 (in Chinese).
Wang, H H„ Peng, J X., Wu, W J., 2003. Huazhong Univ of
Sci and Tech. (Nature Science Edition), 2003, 31 (12), pp. 32-
34.
Wang, Zh., Bovik, A C., 2002. A universal image quality index.
IEEE Signal Processing Letters,9(3), pp.81 - 84.
Xydaes, C., Petrovi, V., 2000. Objecctive image fusion
performance measure. Electronic Letters , 36 (4) , pp.308 -
309.
5. CONCLUSIONS
In this paper, a comprehensive fusion image quality evaluation
model has been proposed to mainly discuss the interpret effect
for many types of ground objects in fusion result image. For
the original images, high-resolution airborne SAR and SPOT5
data, some types of ground objects are easily to be recognized
in the fusion image, such as trees, land use and road according
to the test result, and some other types can only have a weak
function for human visual system. That is to say, different
types of ground objects have different visual behaviour for
human eyes, and this will direct us to choose the right fusion
methods and evaluation model against the practical interpret
objective. As the test result shown, the CFQ model can provide
a reference evaluation index in the processing of fusion image
quality evaluation.
REFERENCES
Di, H W., Liu, X F.,2006. Image fusion quality assessment
based on structural similarity. ACTA PHOTONICA SINICA,
35(5), pp. 767-771.
Hu, L M., GAO, J., HE, K F.,2004. Research on quality
measures for image fusion. ACTA ELECTRONICA SINICA, 32
(12A), pp. 218-222.
Gemma, P., 2004.New quality measures for image fu ion. In:
The 7th International Conference on Information Fusion,
Stockholm, Sweden, June 28 to July 1, pp.542 - 546.
Guo, H D., 2000. Theories and application of radar for earth
observation. Beijing : Science press , 34 (in Chinese).
Pohl, C., 1998. Multi-sensor image fusion in remote sensing:
concepts, methods and applications. Int. J. Remote Sensing, 19
(5), pp.823-854.
Solberg, A H S., Jain, A K., 1997. Texture fusion and feature
selection applied to SAR imagery. IEEE Trans, on Geoscience
and Remote Sensing, 35 (2), pp. 475-479.