1119 
DATA FUSION USING IHS TRANSFORMATIONS FOR EXPLORING ORE DEPOSITS 
IN NORTHEASTERN PART OF THE SAHARAN METACRATON 
A. H. Nasr, T. M. Ramadan 
National Authority for Remote Sensing and Space Sciences, 23 Joseph Broz Tito st., El-Nozha El-Gedida, 
P.O. Box: 1564 Alf-Mascan, Cairo, Egypt - aymanasr@hotmail.com, ramadan_narss2002@yahoo.com 
KEY WORDS: Data Fusion, IHS Transformations, Landsat TM, RADARSAT-1, Saharan Metacraton 
ABSTRACT: 
The main objective of the remotely sensed data fusion is to create an integrated composite image of improved information and 
enhanced interpretability. This data have geospatial details about earth’s surface for substantial assessment of land resources and 
mineral exploration. Fusion of Visible-Infrared (VIR) and Synthetic Aperture Radar (SAR) images provides complementary data to 
increase the amount of information that can be extracted from the individual input images. It contains the details beneath the surface 
cover of the respective SAR data while maintaining the basic color content of the original VIR data. Image fusion can be performed 
at three different processing levels which are pixel level, feature level and decision level according to the stage at which the fusion 
takes place. In this work, a pixel based image fusion from different sensors, namely Landsat TM and RADARSAT-1 was performed 
using the Intensity, Hue, and Saturation (IHS) transformations procedures. The northeastern part of the Saharan Metacraton is 
dominated by medium to high-grade gneisses and migmatites, disrupted by belts of low-grade volcano-sedimentary sequences 
representing arc assemblages and highly dismembered ophiolites and intruded by a-type granitoids. Banded Iron Formation (BIF) 
and gold mineralization are associated with the high grade gneisses and migmatites. According to the fusion results, the fused image 
have enhanced subsurface structures such as foliation, faults and folding that control mineralization of several deposits and reveals 
the fluvial features which are not observable in Landsat TM images. 
1. INTRODUCTION 
Earth observation satellites provide data at a broad range of 
characteristics and multisource imageries including; spectral, 
spatial, and temporal resolutions. By combining these data that 
use different physical principals and record different properties 
of the objects, this may generate datasets that have more 
information than each of the input data alone. This process of 
combining several kinds of imagery is known as data fusion 
(Park and Kang, 2004). Several definitions can be found: "Data 
fusion is capable of integrating different imagery data to 
produce more information than can be derived from a single 
sensor" (Pohl and Van Genderen, 1998). Another 
comprehensive definition: "Data fusion deals with the 
synergistic combination of information made available by 
various knowledge sources such as sensors, in order to provide 
a better understanding of a given scene" (Dasarthy, 1994). The 
benefits from the fused images vary, they may detect the 
changes occurred over a period of time, enhance spatial 
resolution of multispectral images, generate an interpretation of 
the scene not obtainable with data from a single sensor, and 
reduce the uncertainty associated with the data from individual 
sensor (Kim et al., 2005). They generally offer increased 
interpretation capabilities, achieve more specific inferences and 
produce more reliable results. 
Data fusion can be performed at three different processing 
levels namely; pixel level, feature level and decision level. In 
pixel level fusion, the combination mechanism works directly 
on the data obtained from the outputs of sensors. Feature level 
fusion, on the other hand, works bn features extracted from the 
source data or the features which are available from different 
other sources of information. Decision level fusion works at an 
even higher level, and merges the interpretations of different 
objects obtained from different source of information 
(Samadzadegan et al., 2006). In this work we have used pixel 
level image fusion to obtain a new image that has superior 
properties over the individual input images with different 
properties. A general survey of pixel level image fusion 
techniques can be found in (Pohl and van Genderen, 1998). 
In order to enhance some features like spatial and textural 
features as well as features that are not visible in optical images, 
data fusion of optical VIR and SAR imagery is used. VIR 
sensors offer spectral information about terrain cover types, 
while SAR sensors are active sensors and can penetrate 
materials which are optically opaque, and thus not visible by 
optical or IR techniques. Therefore, SAR images complement 
photographic and other optical imaging capabilities to increase 
the amount of information that can be extracted from the 
individual input images (Gungor and Shan, 2006). In this paper, 
we integrated RADARSAT-1 features into co-registered TM 
image using IHS transformations for geological and mineral 
exploration in the study area. It enhanced subsurface structures 
such as foliation, faults and folding. The remainder of the paper 
is arranged as follows: Section two explains the IHS 
transformations with their equations. Section three presents the 
data acquisition and methodology. Where, the data, the 
software used, and the processing steps are introduced. Section 
four focuses on the results and discussions. Finally, our 
conclusions are given in section five. 
2. INTENSITY-HUE-SATURATION 
TRANSFORMATIONS 
The IHS color space is very useful for image processing 
because it separates the color information in ways that 
correspond to the human visual system’s response. It is an