International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
   
TEXTURE ANALYSIS BASED FUSION EXPERIMENTS USING HIGH-RESOLUTION 
SAR AND OPTICAL IMAGERY 
Shuhe Zhao', Yunxiao Luo, Hongkui Zhou, Qiao Xue, An Wang 
School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210093, P.R. China, 
zhaosh@nju.edu.cn 
KEYWORDS: Data Fusion, Texture Analysis, Terrasar-X, Quickbird, Evaluation 
ABSTRACT: 
High resolution SAR images contain plenty of detailed textural features, and optical images have spectral features. For the different 
characteristics of the two images, Firstly, we extracted textural measures of TerraSAR-X image based on the Gray Level Co-occurrence Matrix 
(GLCM) method, and chose the appropriate window. Then data fusion between textural measures of TerraSAR-X image and QuickBird multi- 
spectral image was implemented based on PCA transform, and the fusion results were quantitatively evaluated, showing that the fusion image 
keep spectral information well and the spatial information be enhanced. 
1. INTRODUCTION 
Multi-spectral optical images have abundant spectral information and 
provide good discrimination ability, but acquisition of them is 
affected by atmospheric phenomena, such as cloud cover, smog, haze 
and winter darkness. SAR images can be operated under any weather 
conditions, but their discrimination ability is affected by the presence 
of speckle and single frequency nature [1]. So it is useful to fuse the 
two kinds of data to overcome the disadvantages and obtain more 
information. There are several work have been done on this. However, 
fusion of different sensor data such as those in optical and radar 
imagery is still a challenge [2]. 
In the interpretation of synthetic aperture radar (SAR) images, texture 
provides important information, in addition to image gray levels or 
the backscatter values alone [3]. And also texture may, in fact, be 
more useful than image tone in interpreting radar images [4]. It is 
very important to investigate textural information in SAR images, 
especially for high resolution SAR images, like TerraSAR-X. Gray 
Level Co-occurrence Matrix (GLCM) was put forward by Haralick et 
al. [5], which is the most popular used texture image generation and 
analysis scheme [6]. 
So in this paper, we will firstly analyze the textural information of 
TerraSAR-X image based on GLCM, deicide a proper moving 
window size and extract some textural measures from it. Then fuse 
the textural measures and QuickBird multi-spectral image by 
principal component analysis method. 
2. STUDY AREA AND DATA 
2.1 Study area 
The capital of Jiangsu province in China, Nanjing, has been selected 
as the study site. The area chosen for this study covers a very small 
portion, and situates in the nearby area of Nanjing Yangtze River 
Bridge (32°08'19"N-32°6'4"N, 118°43'26"E-118°46"25"E) (Fig. 1). It 
Covers an area of about 4.8km X 4. 1km, with a variety of land cover. 
  
* Corresponding author. 
    
x S i. 
(a) (b) 
Figure 1. Images of study area, (a) is the Standard false-color 
composite image of QuickBird, and (b) is TerraSAR-X HH image. 
2.2 Data 
In this study, a QuickBird image of June 2007 and a TerraSAR-X 
image of 4 March 2008 have been used. The QuickBird data have 
four multispectral bands and one panchromatic band. In this study, 
green, red and near infrared bands have been used. TerraSAR-X is an 
X-band polarimetric SAR, and the data used in the study is Stripmode, 
at a high resolution of 3 m. 
3. METHODOLOGY 
3.1 Data preprocessing 
Different from optical images, SAR images have a granular 
appearance due to the speckle formed as a result of the coherent 
radiation used for radar systems, which reduces the spatial resolution 
and fine structure of the image, and makes interpretation of SAR 
images more complex. So the reduction of the speckle is a very 
important step in further analysis. The analysis of the radar images 
must be based on the techniques that remove the speckle effects 
while considering the intrinsic texture of the image frame [7]. As the 
noise in SAR images is multiplicative noise, in this study, several 
classical adaptive filters [8] for speckle suppression such as Lee, 
Enhanced Lee, Frost, Enhanced Frost, and Gamma-map were 
compared. The method Lee with 5x5 was chosen, for the speckle 
noise was reduced with very low degradation of the textural 
information.