In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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ASSESSMENT OF VERY HIGH RESOLUTION SATELLITE 
DATA FUSION TECHNIQUES FOR LANDSLIDE RECOGNITION 
L. Santurri a , R. Carla a ’ *, F. Fiorucci b , B. Aiazzi a , S. Baronti a , M. Cardinali b , A. Mondini b 
a IFAC-CNR, Institute of Appled Physics - Italian National Research Council, Via Madonna del Piano 10,1-50019, 
Sesto Fiorentino, Firenze, Italy (r.carla, b.aiazzi, s.baronti, l.santurri)@ifac.cnr.it 
b Istituto di Ricerca per la Protezione Idrogeologica, Via Madonna Alta n. 126,1-06128, Perugia, Italy 
(f.fiorucci, m.cardinali,a.mondini)@irpi.cnr.it 
KEY WORDS: Data Fusion, Ikonos, Landslide, Multispectral Images, Pan-sharpening, Quality Assessment 
ABSTRACT: 
Pan-sharpening is gaining an increasing attention in the remote sensing community, and its usefulness have been demonstrated in 
several environmental applications. A variety of pan-sharpening techniques, aiming at improving the quality of the fused image have 
been proposed in literature, but the ranking of their efficiency is a difficult task since the quality of the pan-sharpened image 
depends on the considered applications. In the literature the IHS-based technique has been proposed as the most effective for 
landslide detection, but in a more generic framework, other methods such as the Gram-Schmidt Adaptive (GSA) and the General 
Laplacian Pyramid (GLP) have been found as most performing than the IHS, together with their improved Context Adaptive 
versions, the GSA-CA and GLP-CA, that relies on local statistics. In the context of the MORFEO project, funded by the Italian 
Spatial Agency (ASI), this work aims at verifying these conclusions by comparing the performances of IHS, GSG and GSA-CA 
methods together with those of the Principal Component (PC) and the widely used Gram Schmidt (GS) methods. The comparison 
have been performed on IKONOS multispectral data, by evaluating the results both in a quantitative and qualitative way. The 
qualitative assessment has been performed by means of a visual assessment in terms of landslide detection by photointerpretative 
techniques. Possible correlation and or differences found among the quantitative and the visual assessment have been analyzed. 
1. INTRODUCTION 
Data fusion techniques are widely applied in the scientific 
community to exploit the potentiality of complementary data 
(Pohl and Genderen, 1998), and in particular the Pan- 
sharpening that is a branch of data fusion devoted to the 
improvement of multispectral data quality by merging 
Multispectral (MS) and Pancromatic (Pan) data characterized 
by complementary spatial and spectral resolution (Chavez, 
1991; Wang, 2005). This is due to the increasing quantity of 
multispectral data acquired by the new spacebome sensors 
(SPOT, IKONOS QuickBird). The usefulness of pan-sharpened 
data have been demonstrated in several environmental 
applications, (Couloigner, et al., 1998; Fanelli et al., 2001; 
Gonzales and Seco, 2002; Yang et al., 2000) and a variety of 
pan-sharpening techniques have been proposed in literature 
(Wang et al. 2005; Chavez et al. 1991; Zhang, 2002), aiming at 
improving the quality (from the qualitative and/or quantitative 
point of view) of the fused images. As a matter of fact, the 
resulting quality of a fused image is related to many factors, 
such as spatial, spectral, radiometric accuracy and feature 
distortion, and therefore different pan-sharpening methods have 
been developed aiming at different goals. 
An important family of pan-sharpening techniques is that of the 
component substitution (CS) methods, such as those based on 
IHS (Carper et al., 1990; Edwards et al. 1994; Liu, 2000; Tu et 
al. 2001), on the Brovey transform (Gillespie, 1987) and on the 
Principal Component (PC) Analysis (Chavez and Kwarteng, 
1989). These methods are fast, have good spatial performances 
and are useful for many visual interpretation tasks (Wang et al. 
2005), but PC and IHS methods are highly sensitive to bands 
misalignment, as it happens for some VHR (Very High 
Resolution) imager such as IKONOS (Zhang, 2004); therefore 
the Gram-Schmidt (GS) technique has been developed to 
improve CS methods accuracy in such context. Concerning the 
spectral quality, these methods generally provide pan- 
sharpening images with a high visual quality, but having often a 
noticeable spectral distortion (colour changes) and differences 
in mean (Alparone, 2007); to partially overcome these 
drawbacks, a generalization of the CS methods has been 
proposed by considering a synthetic intensity generation that 
takes into account the different spectral responses of the 
multispectral bands and the Pan image. 
In fact, a high spectral quality of the pan-sharpened images is 
important for some remote sensing application such as soil and 
vegetation analysis (Liu, 2000, Garguet-Duport et al. , 1996). 
Therefore other methods different from the CS one, such as the 
HPF filter (Chavez et al. 1991; de Béthune et al. 1998) and the 
SFIM (Liu, 2000) have been developed aiming at a better 
performance in terms of spectral fidelity. A statistic based 
fusion method named Pansharp has been also presented by 
Zhang (Zhang, 2002) to mitigate colour distortion and the 
dependency of the data fusion performances on operator skill 
and dataset characteristics. Finally, Multi-Resolution Analysis 
techniques (MRA) have been extensively studied, based on 
performing tools such as the à trous wavelet tansform (AWT), 
and Laplacian Pyramids (AWLP) (Aiazzi, et al., 2002; Ranchin 
et al., 2003). These methods show a potentiality in tuning the 
trade-off among spatial and spectral quality (Zhou et al., 1998), 
at the cost of a most time-consuming process, and critical 
requirement for co-registration accuracy (Liu, 2000). To 
overcome these drawbacks, Aiazzi et al (Aiazzi et al, 2002) 
* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.