COLOUR-BASED AND CRITERIA-BASED METHODS FOR IMAGE FUSION 
Oguz Gungor, Jie Shan 
Geomatics Engineering, School of Civil Engineering, Purdue University, 500 Stadium Mall Drive, 
West Lafayette, IN 47907, USA - {ogungor, jshan}@ecn.purdue.edu 
Commission VII, WG VII/6 
ABSTRACT: 
Panchromatic and multispectral images are useful for the acquisition of geospatial information about the Earth surface for the 
assessment of land resources and environment monitoring. Panchromatic images usually have a better spatial resolution than the 
multispectral ones of the same sensor, while the multispectral images provide spectral properties of the objects. Image fusion 
methods are needed to find the missing spatial details in the multispectral images using the panchromatic ones and transfer these 
details into the multispectral images without or with limited spectral content distortion. This study addresses two classes of image 
fusion approaches: colour-based methods and statistical methods. Specifically, the traditional RGB to IHS transform is generalized 
from 3-D to n-D such that it can handle multiple image bands. As for the statistical methods, we propose a criteria-based approach 
that produces fusion products to meet a set of predefined desired properties. Principles, solutions, and formulation regarding these 
two approaches are presented. The proposed methods are tested with QuickBird images. Fusion results are evaluated visually and 
quantitatively with discussions on their properties. 
1. INTRODUCTION 
Remote sensing sensors capture the energy reflected or emitted 
from the objects and convert it into the digital numbers to form 
images. A sensor has a fixed signal to noise ratio associated to 
hardware design. An object can be detected only if sufficient 
amount of energy reaches the sensor. The energy to be collected 
by the sensor is related, among others, to IFOV (instantaneous 
field of view) of the sensor and the capability of the sensor to 
collect the energy over a certain spectral bandwidth. 
IFOV of the sensor is inversely proportional to the spatial 
resolution of the image collected. The larger the IFOV, the 
lower the spatial resolution, since a sensor with a larger IFOV 
collects energy from a larger area on the ground. On the other 
hand, the amount of energy that reaches the sensor can also be 
increased by collecting the energy over a broader spectral 
bandwidth. This means that reducing the IFOV and increasing 
the capability of the sensor to collect energy over a larger 
spectral bandwidth may retain the spatial resolution of the 
image (Pradhan,2005). Panchromatic sensors collect the energy 
reflected by the objects over a broader spectral bandwidth with 
a narrower IFOV; therefore, panchromatic images have more 
spatial detail content than the multispectral images of the same 
sensor. This is why panchromatic images usually have a better 
spatial resolution than the multispectral images of the same 
sensor. 
Image fusion intends to enhance the spatial details in the 
multispectral images by using the panchromatic ones. Over the 
last two decades various image fusion algorithms have been 
introduced (Pohl and van Genderen,1998). These methods are 
designed to accomplish two main tasks: extract the spatial 
details from the panchromatic image, and transfer them into the 
multispectral image using certain fusion rule or transform. 
Among the various types of image fusion methods, the study 
will address the colour-based methods and statistical methods. 
The most representative colour-based approach is based on the 
RGB to IHS transform. The RGB colour space is ideal for 
colour image generation. Images are displayed on monitors 
using RGB colour system and most image processing 
algorithms use RGB colour space for image processing 
applications. However, it has limitations (Gonzales and 
Woods,2003). The RGB colour space is not intuitive and not 
practical in colour selection. It is almost impossible to 
distinguish a colour from another only with RGB colour 
coordinates. In addition, it is device dependent. Different 
monitors and even an adjustment to the same monitor give 
different results. On the other hand, IHS colour space has a 
significant advantage over RGB colour space. IHS colour space 
makes possible to manipulate each colour attribute individually. 
In a multispectral image, spatial content is retained in the 
intensity component (Chibani and Houacine,2002) and spectral 
information is preserved in its hue and saturation components 
(Pohl and van Genderen,1998; Gonzalez-Audicana et al.,2006). 
Using IHS colour space, the intensity component of an image 
can be changed without modifying its hue and saturation 
components. Due to this property of IHS colour space, it has 
been an ideal tool for image processing applications such as 
contrast enhancement and image fusion where the goal is 
enhancing the spatial content of the image while preserving its 
spectral properties. 
Statistical image fusion methods transfer the spatial detail from 
the panchromatic image using the statistical properties of the 
input panchromatic and the multispectral images. These 
methods include principal component analysis (PCA), linear 
regression method (Price, 1999), spatially adaptive image fusion 
(Park and Kang, 2004) and a-p methods (Gungor and 
Shan,2005, 2006), all of which have clear statistical 
interpretations. 
In this study, we will present two approaches, respectively, in 
the categories of colour-based fusion methods, and statistical 
fusion methods. In the colour based approach, the classical 
RGB-IHS transform will be generalized from 3-D space to n-D 
space so that it can handle any number of bands of multispectral 
images. It is shown that the generalized IHS transform is 
1057