RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDS AT 
7 ETM IMAGERY 
M. Erdogan, H.H. Maras, A. Yilmaz, Ô.T. Ôzerbil 
General Command of Mapping 06100 Dikimevi, Ankara, TURKEY - (mustafa.erdogan; hakan.maras; altan.yilmaz; 
tuncer.ozerbil)@ hgk.mil.tr 
Commission VII, WG VII/6 
KEY WORDS: Aerial Photographs, LANDSAT, Resolution Merge, Image Processing, Remote Sensing 
ABSTRACT: 
Merging of different data sets is often used in digital image processing to improve the visual and analytical quality of the data. The 
analyst may need to merge different types of data. In this process, different data such as satellite imagery from the same sensor but 
with different resolution, satellite imagery from different sensors with varying resolution, digitized aerial photography and satellite 
imagery or satellite imagery with ancillary information can be merged. In this paper, the efficiency of three different merging 
techniques (Principal Component, IHS, and Brovey Transform) is examined, in order to improve the spatial resolution of very high 
resolution aerial photographs (1:35.000 scale panchromatic) with the Landsat 7 ETM imagery. The aim was to get best enhanced 
merged aerial imagery for the visual interpretation. Because of the very big difference between the resolutions of sources, the 
techniques give very different results. The general conclusion is that when the original source imagery is used, Principal Component 
and Brovey Transform merging techniques should be preferred for such kind of imagery. Other methods were also tested to enhance 
the merge imagery, such as, merging the multispectral Landsat 7 ETM imagery with Landsat 7 ETM panchromatic imagery at first 
and merging this imagery with aerial photographs again with three different merging techniques afterwards. In another method, 
multispectral Landsat 7 ETM imagery was resampled to higher resolution imageries and then panchromatic aerial imagery was 
merged with this resampled image with three different merging techniques. In all approaches, Brovey Transform and Principal 
Component techniques serve well the purpose of increasing resolution of the low resolution images with the high resolution images. 
However, all methods should be tested in different areas by using multispectral and panchromatic images which were taken in 
different time frames to define the overall performances of these methods and merging techniques. 
1. INTRODUCTION 
1.1 Resolution Merge 
Digital images taken by airborne or spacebome sensors are very 
frequently used in earth sciences and applications. The 
increasing applications are due to the availability of high 
quality images for a reasonable price and improved 
computation power. Nowadays there is a wide range of systems 
that provide images in digital format, and their interpretation 
into terrestrial attributes is very dependent on their spatial and 
spectral resolution. As a result of the demand for higher 
classification accuracy and the need in enhanced positioning 
precision there is always a need to improve the spectral and 
spatial resolution of remotely sensed imagery. For most of the 
systems, panchromatic images typically have higher resolution, 
while multispectral images offer information in several spectral 
channels. Resolution merge (also called pan-sharpening) allows 
us to combine advantages of both kinds of images by merging 
them into one. 
A variety of resolution merging techniques is available and 
described by several authors. The most common techniques are 
implemented in standard image processing software packages 
(IHS , PCA, Multiplicative and Brovey transforms) It is hard to 
categorise the techniques into a limited number of main types 
(Pohl, C.,1999, Hill et al.,1999, Bretschneider et al.,2004). In 
another study conducted by Carvalho et al.,(2006), a 
geostatistical merging methodology based on direct sequential 
cosimulation with reference images of LandsatTM and SPOT-P 
is tested. With the stochastic simulation one generates a high 
spatial resolution image with the characteristics of the of the 
higher spectral resolution image. It is an iterative inverse 
optimization procedure that tends to reach the matching of an 
objective function by preserving the spectral characteristics and 
spatial pattern, as revealed by the variograms, of the higher- 
spectral resolution images both in terms of descriptive statistics 
and band correlation coefficients. 
Digital image-merging procedures are techniques that aim at 
integrating the multispectral characteristics in a high spatial 
resolution image, thus producing synthetic images that combine 
the advantages of both types of images. The main constraint is 
to preserve the spectral information for tasks like classification 
of ground cover. Ideally, the method used to merge datasets 
with high-spatial resolution and high-spectral resolution should 
not distort the spectral characteristics of the high spectral 
resolution data. Not distorting the spectral characteristics is 
important for calibrating purposes and to ensure that targets that 
are spectrally separable in the original data are still separable in 
the merged dataset (Chavez et al.,1991). Several methods for 
spatial enhancement of low-resolution imagery combining high 
and low resolution data can be used. Some widely used ones are: 
Intensity-Hue Saturation, Principal Component, Multiplicative, 
Color Normalized, and Brovey Transform. Before the 
resolution merge, the source imagery need to be processed to 
have the same reference system. The images have to be 
accurately registered to the same reference system and to one 
another.