1077 
i 
Beijing 2008 
CHANGE DETECTION APPROACH TO SAR AND OPTICAL IMAGE INTEGRATION 
Yu ZENG a ’\ Jixian ZHANG b , J.L.VAN GENDEREN c 
Shandong University of Science and Technology, Qingdao, Shandong Province, 266510, P.R.China 
b Chinese Academy of Surveying and Mapping, Beijing, 100039, P.R.China, zengyu@casm.ac.cn 
c International Institute for Aerospace Survey and Earth Sciences (ITC), 
P.O. Box 6, 7500 AA Enschede, The Netherlands 
Commission VII, WG VII/6 
KEY WORDS: Environmental Monitoring, Change Detection, Fusion, Land Use, Land Cover, SPOT5, RADARSAT 
ABSTRACT: 
In order to overcome the insufficiency of single remote sensing data source during information extraction, to make use of the 
complementary characteristics of SAR data and optical imagery, and to facilitate better monitoring and evaluation of resources and 
ecological environment, this paper develops the idea and presents the approach to land use/cover change detection by different 
temporal SAR and optical image integration. Aiming at the different imaging mechanisms and information characteristics of optical 
imagery and SAR data, this paper employs the object-oriented image analysis technique for high accuracy information extraction 
from high-resolution optical imagery; it proposes a multi-scale and multi-texture feature fusion method based on SVM for 
information extraction from single-band and single-polarization SAR data by employing the multi-scale textural analysis technique 
and the fractal analysis technique; it then integrates individually extracted information of different temporal for change information 
extraction by applying a series of decision rules; it also develops the method for analysis and evaluation of uncertainty of the change 
detection result at the scale of pixels using the extended probability vector. Data adopted in this research are different temporal 
SPOT5 image and Radarsat-1 SAR data. Experimental results prove the correctness, reliability and effectiveness of the methods 
proposed in this paper. 
1. INTRODUCTION 
Timely and accurate change detection of Earth’s surface 
features is extremely important for understanding relationships 
and interactions between human and natural phenomena in 
order to effectively manage and use resources as well as to 
promote better decision making. Because of the advantages of 
repetitive data acquisition, its synoptic view, and digital format 
suitable for computer processing, remote sensing data, such as 
AVHRR, Landsat TM/ETM + , SPOT, IKONOS, SAR and aerial 
photographs, have become the major data sources for different 
change detection applications during the past decades. 
Optical sensors acquire the reflected energy from sunlight 
reaching the ground in the visible and near-infrared spectrum. 
While the multi-spectral data presents the rich spectral 
information of the observed objects, the panchromatic data 
which is often available with a higher resolution than multi- 
spectral data shows detailed geometric information of the 
objects. These features make optical imagery relatively easier 
for interpretation and become the main remote sensing data 
source for change detection. However, due to the limitation of 
data acquisition, such as the impact of clouds, fog, or smoke on 
optical sensors, sometimes, it is difficult to obtain the same 
sensor optical images that meet the temporal requirement. 
Different from optical sensors, a SAR sensor has the all- 
weather capability. Factors which influence the intensity of 
radar returns determine that SAR data can present rich 
structural and texture information, and is sensitive to water 
body, vegetation, and built-up areas. However, due to the 
limited band numbers and polarization modes, as well as the 
affects caused by speckle noises, slant-range imaging, 
foreshortening, layover, and shadows, SAR data is relatively 
difficult for interpretation compared with the optical imagery. 
In order to overcome the insufficiency of single remote sensing 
data source during information extraction, to make use of the 
complementary characteristics of SAR data and optical imagery, 
and to facilitate better monitoring and evaluation of resources 
and ecological environment, this paper develops the idea and 
presents the approach to land use/cover change detection by 
different temporal SAR and optical image integration. Test site 
of this research is located at Jinnan district, Tianjin, China. Data 
adopted are SPOT5 Pan/XS image acquired on October 16, 
2004 and SGF F3 Radarsat-1 SAR data acquired on October 19, 
2005 with the spatial resolution 6.25m. The rest of the paper is 
organized as follows. Section 2 gives an overview on the 
methodology of this research. Section 3 describes the 
experimental results and their analysis and conclusions are 
drawn in Section 4. 
2. METHODS 
2.1 Object-oriented Image Analysis Technique 
Different from pixel-based image classification approach, 
object-oriented image classification operates on objects instead 
of single pixels. These objects consist of many pixels that have 
been grouped together in a certain way by image segmentation, 
and each object is homogeneous and non-intersecting with the 
others. Object-oriented image analysis technique uses shape, 
size, textural, contextual information as well as spectral 
information to perform information extraction at the level of 
objects. While high spatial resolution remote sensing provides