The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
more information than coarse resolution imagery for detailed 
observation on ground objects, single pixels no longer capture 
the characteristics of classification targets, and the increase in 
intra-class spectral variability causes a reduction of statistical 
separability between classes with traditional pixel-based 
classification approaches. Consequently, classification accuracy 
is reduced, and the classification results show a salt-and-pepper 
effect. Because of the characteristics of high resolution imagery, 
lots of object-oriented image analysis was done and research 
results prove the effectiveness of object-oriented image analysis 
for information extraction from high spatial resolution imagery 
(Baatz and Schape,1999; Blaschke et al., 2000; eCognition, 
2002; Benz et al., 2004; Volker, 2004; Yan G et al., 2006). 
An object-based approach to image analysis is composed of 
four steps, which are multi-resolution segmentation to generate 
objects resembling ground objects closely and to create object 
hierarchy which allows a simultaneous representing of image 
features of various scale and establishes a network allowing 
relations between objects to be utilized, image object feature 
extraction and parameter assessment which help to find useful 
features and ways to separate classes, classification which uses 
iterative steps to classify image objects, as well as accuracy 
analysis and evaluation. In this research, object-oriented fuzzy 
classification to optical imagery was performed in Definiens 5.0, 
which is an object-based processing software from Definiens 
Imaging GmbH. 
2.2 Information Extraction from SAR Imagery Based on 
Textural Features 
Texture, a representation of the spatial relationship of grey- 
levels in an image, is an important characteristic for the 
automated or semi-automated interpretation of digital imagery. 
SAR image is the representation of backward scattering 
characteristics of ground objects to radar waves. If the ground 
objects have same or similar backward scattering values, it is 
difficult to get them separated, especially in the single band and 
single polarization SAR imagery. In addition, because of the 
influence of speckle noises, it is hard to distinguish ground 
objects only by intensity values. Textural information should be 
utilized (Guo et al., 2000). 
Nowadays, different methods have been proposed for analysis 
of image texture, and there is no general agreement on an 
overall best analysis method, which outperforms all the others 
on various tasks. In this research, we propose a multi-scale and 
multi-texture feature fusion method based on SVM (Support 
Vector Machine) for information extraction from single-band 
and single-polarization SAR data by employing the multi-scale 
textural analysis technique and the fractal analysis technique. 
The method makes use of the ability of GLCM (Grey Level Co 
occurrence Matrix) which uses spatial correlated characteristic 
of grey values for texture description, takes the multi-scale 
features of ground objects into account, and incorporates multi 
fractal features which have the great capability in description of 
complex spatial structure information and detailed texture 
features; on the basis of SVM and by using feature-level image 
fusion technique, it integrates seven textural features to realize 
the land use/cover classification of SAR imagery. SVM is 
introduced in this research because it is established on the basis 
of statistical learning theory, and has favourable classification 
performance in the feature space which is nonlinear, high 
dimensional and has small samples. The seven-dimensional 
textural features are: 
Feature 1: Correlation feature with the processing window size 
11; 
Feature 2: Standard Deviation feature with the processing 
window size 21 ; 
Feature 3: Entropy feature with the processing window size 13; 
Feature 4: Fractal dimension feature; 
Feature 5-6: two multi-fractal features; 
Feature 7: Second-order statistic lacunarity; 
Where, the first three multi-scale GLCM features are obtained 
according to multi-scale textural analysis, which can be referred 
to Zeng et al.(2007a); the latter four features are based on 
fractal theory and are obtained according to fractal analysis. 
The calculation of fractal dimension feature can be referred to 
Zeng et al.(2007b). 
Improving the interpretation accuracy of SAR data is the basis 
for change detection with optical imagery at the level of 
information processing. 
2.3 Soft-decision Change Detection Based on Rules 
On the basis of above research, the change information can be 
obtained by combining individually extracted information of 
different temporal. Aiming at the change overestimation caused 
by error propagation and the traditional hard-decision method 
for change detection, this research proposes a soft-decision 
method based on rules for change detection. In China, land 
use/cover change usually occurs at the urban fringe areas, and 
most land use/cover changes are the consequence of urban 
growth. In this instance, we can think that change to built-up 
area from other land use types is irreversible. With the rapid 
economy development and population growth, the emphases of 
land use change monitoring in China are urban expansion and 
decrease of plantation areas. Taking the characteristics of land 
use change at the urban fringe areas and the research focus into 
account, this method employs a series of logic rules in turn to 
evaluate the rationality of the detected changes. The logic rules 
are made according to the status of pixels which are changed or 
unchanged, land use change trajectories, as well as by taking 
the spatial features of detected changes which are shape, size 
and location into consideration. 
Let Num denotes the number of detected changes. Land use 
types are presented as Ci. Here, C! = “built-up area”, C 2 = 
“water”, C 3 = “vegetation”, C 4 = “bare land”. The detected 
change trajectory is denoted as j{C i ,,...)• Let N refers to “no 
change”, W refers to “wrong classification and applying 
masking”, and Y refers to “correctly detected changes”. These 
logic rules are: 
Rule 1 : if Num = 0 THEN N. 
Rule 2: jp Num = \ AND r(C,.,C y ) (i * 1; / * j) THEN Y. 
Rule 3: IF Num = \ AND T(C t ,Cj) 0 *1) THEN W. 
Rule 4: River, lake, canal and its affiliated works are regarded 
as N. 
Rule 5: Land use types in between large area of cultivated lands 
are regarded as N. 
Rule 6: Isolated 3x3 detected change regions are regarded as W. 
By employing these rules in turn to analyze the rationality of 
the detected changes, the unchanged areas, falsely detected 
changes, and possible changes can be separated. The change 
overestimation can be decreased and the change detection 
accuracy can be improved. 
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