International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
consequence that feature extraction, segmentation, 
classification, plays a important rule in particular for decision 
fusion. From an application point of view this addresses 
problems of automating mapping procedures and map update. 
[n the following we will focus in image fusion techniques on the 
pixel level. The most well-known techniques, the IHS and PCA 
methods for colour composing, are already implemented in 
remote sensing packages; but some more advanced methods are 
methodologically or technically not yet mature. Altogether ten 
techniques will be mathematically described in the next section. 
All algorithms are implemented in MATLAB with the idea to 
create a MATLAB fusion toolbox. Experiments with IRS-1C 
and ASTER images are presented and discussed in Section 4. 
We finally conclude with a short summary and 
recommendations for future research. 
2. IMAGE FUSION TECHNIQUES 
The number of proposed concepts for image fusion is growing 
which indicates ongoing research in this area. Technically, 
image data recorded by different sensors have to be merged or 
composed to generate a new representation. Alternatively data 
from one sensor are also subject of image fusion. Different 
multispectral channels are to be considered as different sources, 
as well as images taken at different times by the same sensor. 
The goal of all image fusion techniques is obtain information of 
greater quality which may consist of a more accurate description 
of the scene than any of the individual source images. This 
fused image should be more useful for human visual inspection 
or machine perception. The sensors used for image fusion need 
to be accurately co-aligned. Alternatively images from different 
sources may have to be registered or geocoded to the reference 
coordinate system. 
References for the algorithms worked out in the following are 
Anderson (1987); Burt (1992); Carper et al. (1990); Chavez et 
al. (1991); Kathleen and Philip (1994), Rockinger (1996) and 
Wald (2002). With respect to the conceptual approach, we 
distinguish the proposed techniques into eight classes of IHS, 
PCA, SWDT, Laplacian and FSD Pyramid, Contrast pyramid, 
Gradient pyramid. Selection and simple Averaging process. The 
main characteristics of these techniques are discussed in the 
context of its mathematical formulation. 
Intensity-Hue-Saturation (IHS) 
1.1 Fusion based on 
method 
The Intensity-Hue-Saturation method (IHS) is one of the most 
popular fusion methods used in remote sensing. In this method. 
three multispectral bands R, G and B of low resolution are first 
transformed to the IHS colour space: 
pi sisi uos mat 
T 3 3 3 [R 
visi: 2 Ie (1) 
EEE uei ul Qu 
Sus gore uer Im 
2 l I 
42 
zh V 
H’‘="tan 2.1 (2) 
Vi 
Fa 5 
S = VV + V5 (3) 
Here / is intensity, H is hue, S is saturation, and V; .V, are 
intermediate variables. Fusion proceeds by replacing / with the 
panchromatic high-resolution image of another source. The 
fused image is then obtained by performing an inverse 
transformation from IHS back to the original RGB space 
according to 
l I 
ia aa 
R) se V1 
yi Ld 
G |l Ne ro, Vi (4) 
B 9 Vv, 
] -— 0 
For some more discussion of this standard technique please 
confer to Carper et al. (1990). 
2.2. Fusion based on Principal Component Analysis method 
Principal component analysis (PCA) is a general statistical 
technique that transforms multivariate data with correlated 
variables into one with uncorrelated variables. These new 
variables are obtained as linear combination of the original 
variables. PCA has been widely used in image encoding, imag 
data compression, image enhancement and in image fusion. 
Applied to image fusion, PCA is performed on the image with 
all its spectral bands. An orthogonal colour coordinate system 
for PCA is derived by 
PCI] (Pi 95 Piz | R 
PGC2|-|o 21 o 22 o 53 G (5) 
PCY "(Pu PR P;3 | B 
M ; 
24 .. SzJPC2! c PC. {0 
P 
PC? 
  
H = mí 
X 
Hue (H) and saturation (S) defined here are different to values 
obtained by IHS (Eq. 2). The transformation matrix d^ with 
elements 9 ;; consist of the eigenvectors of the covariance 
matrix R with terms rj and the transformation matrix satisfies 
the relationship 
ERP’ =A (7) 
where A = diag(A, ,A,,A3) are eigenvalues corresponding 
to? organised in descend order. The procedure to merge the 
RGB and the Pan image using the PCA method is similar to (he 
IHS method. That is, the first component (PCI) of the PCA 
space is replaced by the Pan image and retransformed back in? 
the original RGB space: 
Ru o, o, PD, pan 
d = o. o. P,; PC) (8) 
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