Angular Second Moment (ASM):
YLpüjÿ
i i
(3)
Standard Deviation (SD):
Variance = £ Z (*>’)/»(*. J) ~ P x M y ^
SD=SQRT(Variance) (5)
Contrast (CON):
'ZÎ.Q-iïpff.j) <6)
> i
Dissimilarity (DIS):
YL\i-j\Püj)
(7)
Entropy (ENT):
“Z Z ^’7) l°ë,(p(hj))
i j
(8)
Correlation (COR):
Z Z (ü)p(^j)-p x p
y
(9)
2.2 Parameters Set and Selection
In order to properly use the co-occurrence method for image
texture analysis, several variables should be considered: the
number of quantization levels, the number and type of
measurements, the window size to analyze, the pixel pair
sampling distance and orientations.
For quantization levels, Soh and Tsatsoulis (1999) indicate that
a complete grey-level representation of the image is not
necessary for texture mapping, an eight-level quantization
representation is undesirable for textural representation, and
setting quantization levels to 64 is efficient and sufficient.
Clausi (2002) recommends setting quantization level to 24.
However, larger values of quantization level greater than 64 are
deemed excessive. Here in our research, we use 64 as the
quantization level.
Because there is no obvious structure characteristics of SAR
imagery, and it presents isotropic behaviour, directional
invariant texture measures which are the averages among
texture measures for 4 directions (0°, 45°, 90°, and 135°) are
advocated.
Window size and pixel pair sampling distance are key two
parameters that affect the extracted texture feature from GLCM.
It reaches different conclusions from different research. These
two factors will be studied detailedly in this research.
remote sensing imagery. When we apply the GLCP method to
describe texture, window size to process determines the ability
to capture texture features at different spatial extents. In general,
smaller window size could be easily influenced by SAR noises
while it can describe small texture feature; lager window size
could describe the whole scenery better and not easily
influenced by SAR noises but cannot describe small texture
features. This comes our idea to integrate multi-scale texture
features in order to accurately capture the textural
characteristics of a given surface and reach a better
classification performance.
In the multi-scale analysis, the selection of scale is a key issue.
This paper presents a method that using semi-variogram model
to estimate the scale of the ground objects. Semi-variogram is a
feature which is based on the spatial autocorrelation. Let the
grey levels comprising a given digital image be represented as
G(x,y). Then, the semi-variogram for these grey levels is
defined as:
rW=^i,Mx,y)-G(x,y)] 2 OO)
where, h is the Euclidean distance (lag distance) between the
pixel value G at row x, column y , and the pixel value at row x’,
column y\ Figure 1 is an example of a semi-variogram model.
Figure 1. An example of a semi-variogram model
This method is based on the assumption that a regional variable
becomes random at a great distance, i.e. the correlation of a
regional variable with other pixels will normally decrease with
increasing distance. Thus we use it here for estimate the spatial
structure of the image.
Non-parametric classifier, Fuzzy K-means, is used as the testing
classifier, and non-parametric classifier, K-Nearest
Neighbourhood is used as the final classifier.
3. EXPERIMENTAL RESULTS AND DISCUSSION
Image dataset used here is Radarsatl fine beam mode with the
spatial resolution 6.25m (C-band with HH polarization). Land
use/land cover types concerned are vegetation, residential area,
and water body.
3.1 Texture Feature Selection
The last consideration is the number and type of measurements
taken from the co-occurrence matrices. In our research, 7
texture measures mentioned above will be processed and
analyzed.
2.3 Multi-scale Texture Analysis
Different ground object has different scale, and this scale can
also be represented by the texture primitive element in the
Feature selection is to take a set of candidate features and select
a subset that performs the best under some classification system.
This procedure can reduce not only the cost of recognition by
reducing the number of features that need to be collected, but in
some cases it can also provide better classification accuracy.
Firstly, the texture features can be classified into three groups
according to the structure they reveal and their inter-feature
correlations. The first group contains homogeneity, angular
second moment, and entropy, which are the homogeneity
statistics and measu;
the GLCM. The s<
contrast, and dissi
smoothness of the
highly correlated. 1
statistics, which is £
with any of the othei
HOMO
CON
HOMO
1.000000
-0.475
CON
-0.475280
1.000
DIS 1
-0.683933
0.960
SD
-0.528559
0 946
ENT
-0.977448
0455
ASM
0 851364
-0 311
COR
0.216479
-0 205
Table 1. Correia
Secondly, the selecti
group is based on
texture features. Wi
unlike other featur
deviation texture im;
a certain statistical
pair sampling distan
Figure 2. Texture 1
