Full text: Mapping without the sun

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 
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- 
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 - 
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. 
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.

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