961 
Beijing 2008 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
.TISCALE 
nt scales in an 
iningful image 
e problem to be 
jjects should be 
mentation aims 
evels.Generally 
S procedure on 
stimation-based 
nt steps, region 
which acquired 
it this iteractive 
:ion procedure 
ir and M ) of 
ollows: 
ice input and 
iin,Z., is the z'th 
are bandwidth 
jrging region. 
; from RGB to 
; and store all 
meet all of the 
al and spatial 
ameter M and 
a algorithm 
IFICATION 
which aims at 
maximize the 
based on 
ind Structural 
irse sampling, 
ial optimum 
good as or 
g methods in 
most cases. The SVM method separates the classes with a 
hyperplane surface to maximize the margin among them (see 
Fig. 4), and m is the distance between H\ and H2, and 
H is the optimum separation plane which is defined as: 
w-x + b = 0, (1) 
where x is a point on the hyperplane, w is a n-dimensional 
vector perpendicular to the hyperplane, and b is the distance of 
the closest point on the hyperplane to the origin. It can be found 
as: 
w• Xj+b<-1, fory. = -l (2) 
w-x i +b> 1, for y. = +1 
(3) 
These two inequalities can be combined into: 
Vz. (4) 
The SVM attempts to find a hyperplane (1) with minimum 
|| W 11 2 that is subject to constraint (4). 
H2 
Figure 4. Optimum separation plane. 
The processing of finding optimum hyperplane is equivalent to 
solve quadratic programming problems: 
m in^|| H| 2 
S.t. yXw-fix^ + b^X-^ (5) 
£>0,/ = l,2,...,/ 
where C is penalty parameter, which is used to control the edge 
balance of the error ^. Again, using the technique of Lagrange 
Multipliers, the optimization problem becomes: 
1 / / / 
min V Z Z a i a jyiy j K ( x t .yj-X 
1 /=1 № /=l 
sjm Sw=° 
1=1 
0 < a, < C,i = 1,2,...,/ (6) 
where K(x i ,y j ) = f(x i )-</)(y / )is kernel function. There are 
three major kernel functions including Gaussian Radius Basis 
Function (RBF), Polynomial, and Sigmoid function. The 
optimum classification plane is solved through chunking, Osuna, 
and SMO algorithms, and then we will only need to compute 
K{x,,yj) * n the training process. The final decision function is: 
f{x) = sgn(^^.«,/:(x, x,) + b) (7) 
SV 
When multi-class SVM is concerned, three basic methods are 
available to solve the classification: One-Against-All (OAA), 
One-Against-One (OAO), and Directed-Acyclic-Graph (DAG). 
3.3 OBJECT OVERLAY ANALYSIS(OOA) 
OOA namely polygon overlay analysis creates new features and 
attribute relations by overlaying the features from two input 
layers. Features from each input layer are combined to create 
new output features. Attributes of each input feature are 
combined from the two input layers to describe each new output 
feature, thus creating new attribute relationships. 
Boolean Algebra is useful for performing operations on the 
attributes attached to object entities in the format of vector data. 
Boolean algebra uses the logical operators AND, OR,NOT to 
determine whether a particular condition is true or false. The 
AND operator is the intersection of two sets - for example those 
object entities that belong to both set A and set B. The OR 
operator is the union of two sets - for example those entities that 
belong to either set A or to set B. The NOT operator is the 
difference operator identifying those object entities that belong 
to A but not B. 
OOA is analogous to the boolean logical operator OR, where all 
elements from both input layers will be present in the output 
layer,which is illustrated in the following example. 
Figure 5. OOA sketch map.For areas a,f,g,l,the attributes are 
1,2,3,4 from the first layer.And areas b,d,h are unchanged since 
the attributes from two layers are the same, and reverse for the 
rest areas. 
4. TEST CASE 
The proposed OLCD methodology was tested on the SPOT-5 
and IKONOS images in order to assess its performance.The 
result was compared to a robust pixel-level post-classification 
comparison method. 
4.1 Object-level change detections 
The multidate segmentation was carried out from multisource 
HR images using MS&RG multiscale segmentation 
methodology. The segmentation parameters hs, hr were both set 
at 50,20 for the before and after event image,according to the 
“Try&Error” strategy. The parameter M was separately set at 
250 and 300 to obtain segmented image with a minimum object 
size. 
After the segmentation, we selected spectra(mean) and 
texture(contrast) characteristic of several objects from each 
class as the training samples, taking advantage of statistic 
methods and Gray Level Co-occurrence Matrix (GLCM) 
filtering.During the classification processing, firstly, we scaled 
the training samples and the testing samples before applying 
SVM. Secondly, we chose RBF kernel, which is more suitable 
to land use and land cover classification. Thirdly, we used 
cross-validation and grid-search method to get the best 
parameters of RBF. The highest overall accuracy on the 
multi-source image information was obtained with y =0.3,0.25 
and C=100, 130,respectively,where/is the width of the kernel 
function, and C is the penalty parameter. At last, the training