In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
» 
496 
1997) have been evaluated by using five score indexes, namely 
the ERGAS, the SAM, the RMSE, the PSNR and the 
correlation value, usually adopted for such task, whereas the 
qualitative assessment have been performed by a visual 
inspection of skilled photointerpreters. This analysis has been 
focused on the characteristics of the image useful for landslide 
detection, such as linear features, textures, contrast and colour. 
Quantitative assessment confirms the result of some previous 
comparative works: the GSG and GSA-CA pan-sharpening 
techniques have been found as the most performing, and the 
performances of the GS method is however higher than those of 
the PC and the GIHS ones, because of a residual misalignment 
among panchromatic and multispectral IKONOS bands. On the 
opposite side, the visual analysis does not agree with the 
quantitative conclusions; as a matter of fact the GS method has 
been found as the most performing for the landslide detection 
tasks together with the PC. The GSG shows a similar high 
quality but presents some problems concerning the quality of 
the colour useful for landslide recognition, whereas the GSA- 
CA slightly suffers for some changes in linear features and 
textures useful in landslide detection task. As a consequence of 
the comparison among the quantitative and the qualitative 
assessment, it has been found that the procedures and the score 
indexes often proposed for the assessment of pan-sharpened 
images quality are not fully suitable for the ranking of the 
fusion techniques when landslide detection with 
photointerpretative techniques task is considered. 
8. REFERENCES 
Aiazzi B., Alparone L., Argenti F., and Baronti S., 1999. 
Wavelet and pyramid techniques for multisensor data fusion: A 
performance comparison varying with scale ratios, Proc. SPIE, 
EUROPTO Series Image Signal Process. Remote Sensing V, S. 
B. Serpico, Ed., vol. 3871, pp. 251-262. 
Aiazzi B., Alparone L., Baronti S., and Garzelli A., 2002. 
Context-driven fusion of high spatial and spectral resolution 
images based on oversampled multiresolution analysis, IEEE 
Trans. Geosci. Remote Sensing, vol. 40 (10), pp. 2300-2312. 
Aiazzi B., Alparone L., Baronti S., Garzelli A., and Selva M., 
2006. MTFtailored multiscale fusion of high-resolution MS and 
Pan imagery, Photogramm. Eng. Remote Sens., vol. 72 (5), pp. 
591-596. 
Aiazzi B., Baronti S., and Selva M., 2007. Improving 
component substitution Pansharpening through multivariate 
regression of MS+pan data, IEEE Trans. Geosci. Remote Sens., 
vol. 45 (10), Oct., pp. 3230-3239. 
Aiazzi B., Baronti S., Lotti F., and Selva M., 2009. A 
Comparison Between Global and Context-Adaptive 
Pansharpening of Multispectral Images, IEEE Geoscience And 
Remote Sensing Letters, Vol. 6 (2), 302-306. 
Alparone L., Baronti S., Garzelli A., and Nencini F., 2004. A 
global quality measurement of pan-sharpened multispectral 
imagery, IEEE Geosci. Remote Sens. Lett., vol. 1 (4), pp. 313— 
317. 
Alparone L., Wald L., Chanussot J., Thomas C., Gamba P., and 
Mann Bruce L., 2007. Comparison of Pansharpening 
Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest, 
IEEE Transactions On Geoscience And Remote Sensing, Vol. 
45 (10), pp. 3012-3021. 
Alparone L., Aiazzi B., Baronti S., Garzelli A., Nencini F., and 
Selva M., 2008. Multispectral and panchromatic data fusion 
assessment without reference, Photogramm. Eng. Remote Sens., 
vol. 74 (2), pp. 193-200. 
Carper W. J., Lillesand T. M., and Kiefer R. W., 1990. The use 
of intensity- hue-saturation transformations for merging SPOT 
panchromatic and multispectral image data, Photogramm. Eng. 
Remote Sens., vol. 56, pp. 459-467. 
Chavez P. S. and Kwarteng A. Y., 1989. Extracting spectral 
contrast in Landsat Thematic Mapper image data using 
selective principle component analysis, Photogramm. Eng. 
Remote Sens., vol. 55 (3), pp. 339-348. 
Chavez, Jr. P. S., Sides S. C. and Anderson J. A., 1991. 
Comparison of three different methods to merge multiresolution 
and multispectral data: Landsat TM and SPOT panchromatic, 
Photogramm. Eng. Remote Sens., vol. 57 (3), pp. 295-303. 
Couloigner I., Ranchin T., Valtonen V. P., and Wald L., 1998. 
Benefit of the future SPOT-5 and of data fusion to urban road 
mapping, Int. J. Remote Sens., vol. 19 (8), pp. 1519-1532. 
de Béthune S., Muller F., and Donnay J. P., 1998. Fusion of 
multi-spectral and panchromatic images by local mean and 
variance matching filtering techniques, Fusion of Earth Data, 
Jan. 28-30. 
Edwards K. and Davis P. A., 1994 . The use of Intensity-Hue- 
Saturation transformation for producing color shaded-relief 
images, Photogramm. Eng. Remote Sens., vol. 60 (11), pp. 
1369-1374. 
Fanelli A., Leo A., and Ferri M., 2001. Remote sensing images 
data fusion: A wavelet transform approach for urban analysis, 
in Proc. Joint IEEE/ISPRS Workshop Remote Sens. Data 
Fusion Over Urban Areas , pp. 112-116. 
Garguet-Duport B., Girel J., Chassery J.-M., and Pautou G., 
1996. The use of multi-resolution analysis and wavelets 
transform for merging SPOT panchromatic and multi-spectral 
image data, Photogramm. Eng. Remote Sens., vol. 62 (9), pp. 
1057-1066. 
Gillespie A. R., Kahle A. B., and Walker R. E., 1987. Color 
enhancement of highly correlated images—-II. Channel ratio 
and ‘chromaticity’ transformation techniques, Remote Sens. 
Environ., vol. 22, pp. 343-365. 
Gonzales Audicana M. and Seco A., 2003. Fusion of 
multispectral and panchromatic images using wavelet 
transform. Evaluation of crop classification accuracy, in Proc. 
22nd EARSeL Annu. Symp. Geoinformation Eur.-Wide Integr., 
Prague, Czech Republic, 4-6 June 2002, T. Benes, Ed., 2003, 
pp.265-272. 
Laben C. A. and Brower B. V., 2000. Process for enhancing 
the spatial resolution of multispectral imagery using Pan- 
sharpening, U.S. Patent 6 011 875, Jan. 4, 2000. Tech. Rep., 
Eastman Kodak Company.