The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
forest,building,mountanious region and so on.In the recent 
years,land cover changes are more frequent because of activities 
of human beings in Jiaxing. Four cloud-free panchromatic and 
multispectral SPOT5 and IKONOS images were acquired over 
four years and are considered as our multidate data set. The 
acquisition date of these images were July 17th 2002(SPOT-5 
pan),August 24th 2002(SPOT-5 multispectral), August 10th 
2006(IKONOS pan) and July 18th 2006(IKNONS 
multispectral).The spatial resolutions of above images are 
different,2.5m for SPOT-5 pan,5m for SPOT-5 multispectral,lm 
for IKONOS pan and 4m for IKONOS 
multispectral,respectively. 
Two preprocessing steps were required for a meaningful 
comparison of the satellite images. First, coregistration and 
fusion operations between the panchromatic and multispectral 
images of the same date were carried out. And then images from 
different sensors were registrated to each other with high 
precision to avoid misregistration errors inducing false change 
alerts.Depending on the satellite image, a set of 49GCPs spread 
over the whole study area were selected from satellite images. 3 
Figure 1. Fused images left:SPOT-5(2.5m) right:IKONOS(lm) 
3. OBJECT-LEVEL METHODOLOGY 
OLCD is based on object-oriented analysis technique,the 
principle of which is post classification comparison 
method.Firstly, Image segmentation partitions an image into 
groups of pixels, hereafter named as objects that are spectrally 
similar and spatially adjacent, by minimizing the within-object 
variability compared to the between-object variability.Secondly, 
we classify those objects to thematic map and outline the 
polygon objects(vector data format).And lastly,we apply overlay 
analysis to objects and estimate whether change or not and 
further change type according to the object class attributes. 
Figure 2. The flowchart of OLCD based on MS&RQSVM and 
OOA 
3.1 MEAN SHIFT& REGION GROWMULTISCALE 
SEGMENTATION 
Objects of interest typically appear on different scales in an 
image simultaneously. The extraction of meaningful image 
objects needs to take into account the scale of the problem to be 
solved. Therefore the scale of resulting image objects should be 
free adaptable to the scale of task.Multiscale segmentation aims 
to analysize objects in different scale levels.Generally 
speaking,in this algorithm,we firstly operate MS procedure on 
the images which is the density estimation-based 
non-parametric clustering approach.In subsequent steps, region 
grow technique merged smaller image objects which acquired 
from MS procedure into bigger ones. Throughout this iteractive 
clustering process, the underlying optimization procedure 
minimizes the weighted heterogeneities (hs, hr and M ) of 
resulting image objects. 
The multiscale segmentation algorithm(Fig.3) as follows: 
Given Xj, Zj(/=1,2, ,«)are J-dimentional space input and 
filtered image pixels in spectral and spatial domain,!, is the ith 
labeled pixel after segmentation,and hs, hr and M are bandwidth 
in spatial and spectral domain and minimum merging region. 
(1) Input image data set and transform color space from RGB to 
LUV, 
(2) Carry out MS procedure in LUV color space and store all 
convergent data points to Z\, 
(3) Describe the clustering {Cp} p=l,2, m,connect all of the 
zj which are smaller than hs and hr in spectral and spatial 
domain, 
(4) LrlpZjzCp),for i=l,2, n, 
(5) Merge adjacent objects according to the parameter M and 
obtain segmented images. 
Figure 3. The flowchart of multiscale segmentation algorithm 
3.2 SUPPORT VECTOR MACHINE CLASSIFICATION 
SVM is a useful technique for data classification,which aims at 
seperating classes with an optimal hyperplane to maximize the 
margin among them (see Fig. 4).It is based on 
Vapnik-Chervonenkis (VC) dimension theory and Structural 
Risk Minimization (SRM) rule, which solves sparse sampling, 
non-linear, high-dimensional data, and global optimum 
problems.Its performance has been proved as good as or 
significantly better than that of other competing methods in