959 
2008 
OBJECT-LEVEL CHANGE DETECTION FOR MULTI-TEMEPORAL 
HIGH-RESOLUTION REMOTE SENSING IMAGERY 
Mu H. Wang a b , Ji.X Zhang 3 , Hai T. Li a , Hua L.Xu ab 
a China Academy of Surveying and Mapping,Beijing,l 00039,China-(zhangjx, lhtao)@casm.ac.cn 
b Liaoning Technical University ,School of Geomaticas,Fuxin, 123000,China- amudc@126.com, 
hualong_0795@ 163 .com 
Commission VII, WG VII/5 
KEY WORDS: Change Detection, HR images, Pixel-level, Object-level, Segmentation, Classification 
ABSTRACT: 
A new object-level change detection(OLCD) approach, combining object analysis with change detection process is proposed for land 
surface monitoring. The object analysis is consisting of Mean Shift(MS)&Region Grow(RG) multiscale segmentation, Support 
Vector Machine(SVM) classification and object footprint. Change detection process is composed of object overlay 
analysis(OOA),class attributes comparison and accuracy assessement.Depending on this approach,we can detect the change type of 
objects according to classification label. Furthermore,object boundaries are extracted assisted on the vector tool, and the detection 
result in the form of vector data can be used to update GIS database in the land use/cover(LUCC) change. The OLCD approach 
performances were assessed using multisource SPOT-5 and IKNOS reference data in Jiaxing, and were compared to a pixel-based 
method using post classification comparison in CASMImgeInfo3.5.High overall accuracy(>85%) was achieved by object-level 
method.The experiment result illustrated the approach could make full use of contextual information of objects and effectively detect 
object changes. 
1. INTRODUCTION 
Remote sensing technique has been widely used in the field of 
change detection because of the advantage of macroscopy,high 
speed and short interval of acquiring resources,ample 
information and effective usability(Massonnet et al., 1993).In 
the history of remote sensing applications, many change 
detection techniques have been developed.They can broadly 
grouped into four categories: visual interpretation 
approaches,pixel-level change detection(PLCD),feature-level 
change detection(FLCD)and object-level change 
detection(OLCD). 
Specifically,visual interpretation requires human 
experience(computer-assisted or not) to label zones that are 
considered as changed,which can make full use of 
analysts’experience and knowledge but is time-consuming(Mu 
H. Wang et al.,2007). 
PLCD approaches extract spectral information of pixels to 
describe the geography pattern,and the spatial or contextural 
information between proximate pixels is most often 
ignored(Atkinson et al.,2000, Townshend et al.,2000).With the 
improvement of imagery resolution,the single pixel can’t 
represent a region or object but a part.Espectially for urban 
areas, the phenomenas that are different object with the same 
spetra characteristics and different spetra characteristics with 
the same object are severe because of the 
materials,furthermore,the effect of projection and shade must be 
taken into accounted.All of those make it noninteresting to 
analysize object change in the manner of pixel-level 
methods(Shackelford A.K., Davis,C.H> 2003; Wang Jianmei et 
al.. 2005). Image analysis aims to interpret, quantize and 
describe landscape,while the basic unit of landscape is spatially 
homogeneous parcel,which composes multiscale interestint 
objects.The differences between parcels are 
spectra,texture,shape and spatial layout information,which can’t 
be provided by a single pixel. 
FLCD methods extracted many kinds of features from images 
by means of some information extraction techniques such as 
Principal component analysis(PCA),texture analysis,shape 
analysis,vegetation index,wavelet analysis and so on.And then 
we compared those features to decide whether change or 
not.Though FLCD has advantages in feature attributes 
comparison,while it can introduce other errors in the process of 
information extraction. 
Recently, pursuers pay more attention on object level image 
analysis technique which is similar to visual interpretation. 
Instead of analysing pixels independently of their location, 
similar contiguous pixels are grouped into objects.The interest 
for OLCD methods has increased with the improvements in 
image segmentation techniques. The main advantage of 
object-based methods is the incorporation of contextual 
information in the change analysis (Flanders et al.,2003). 
Moreover, the segmentation reduces the local spectral variation 
inducing better discrimination between land cover types (Lobo, 
1997). However, although the object delineation remains crucial, 
a limitation is the definition of a Minimum Mapping Unit 
(MMU). Therefore,the final result is largely determined by 
objects delineation(Baudouin Desclee et al.,2006). 
This research aims to develop a new OLCD methods to detect 
land cover and land use change in Jiaxing,Zhejiang Province, 
taking advantage of Mean Shift & Region Grow(MS&RG) 
multiscale segmentation,Support Vector 
Machine(SVM)classification,Object Overlay Analysis(OOA). 
This study also aims to test this new approach on a 
multitemporal SPOT-5 and IKONOS data set and to compare its 
performances to the pixel-level method using the post 
classification comparison technique. 
2. STUDY SITE AND DATA 
The city of Jiaxing covers 5282km 2 and is located in the 
southern of Changjiang Delta of China.There are various of land 
use type,including agriculture,water body, 
Mu H.Wang :Phone : 86 10 88217730; Fax: 86 10 68211420; http:// www.casm.ac.cn