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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
The paper is organized as follows. In section one some 
background and the objective of this research are introduced. 
Section two introduces the feature extraction method in this 
paper. The segmentation methodology is presented in section 
three. In section four, we carry out several experiments and 
demonstrate the segmentation results. Section five concludes 
the paper with discussion and conclusion. 
2. FEATURE DESCRIPTION 
In the whole segmentation process, we utilize a novel texture 
and spectral feature extraction method which considers the 
cross-band relations between pixels. Principal Component 
Analysis (PCA) is adopted in this study to get rid of redundant 
information and make it convenient to extract texture features 
of multispectral images. More specifically, we obtain the first 
two principal components (PCs) of multispectral (i.e. blue, 
green, red and near infrared) H-res satellite imagery through 
PCA. They collect most of the information of the H-res 
imagery. Then the texture and spectral information is calculated 
from the transformed PCs. The distributions of texture and 
spectral information are denoted by discrete two-dimensional 
histograms whose two dimensions correspond to the two PC 
variables respectively. 
2.1 LBP texture operator 
The texture analysis operator of LBP (Local Binary Pattern) 
was first introduced as a complementary measure of local image 
contrast by Ojala et al. (Ojala et al. 1996) and was extended by 
subsequent studies. Due to its major advantages on simple 
theory, computational simplicity and robustness to rotation and 
monotonic transformation of gray scale, it has been frequently 
used in many studies, such as texture segmentation or 
classification (Chen and Chen 2002; Ojala et al. 2006), moving 
objects detection (Heikkila and Pietikainen 2006) and 
segmentation of remote sensing imagery (Hu et al. 2005; 
Lucieer et al. 2005). 
The name “Local Binary Pattern” reflects the functionality of 
the operator, i.e., a local neighborhood is thresholded at the 
gray value of the center pixel into a binary pattern (Ojala et al. 
2002). The original LBP was produced by multiplying the 
thresholded values with weights given to the corresponding 
pixels, and summing up the results (Maenpaa 2003). Ojala et al. 
(Ojala et al. 2002) proposed gray-scale invariance LBP 
form LBP p r , which is defined as 
P-1 
LBP 2' 
(1) 
P=0 
Where 
s „= s (g P -g c ) = 
o, g P < g. 
1> g D ^g c 
(2) 
Where P is the number of neighboring pixels on a circle of 
radius R, g c corresponds to the gray value of the centre pixel 
of a texture unit and g p is the gray value of its neighbourhood. 
In order to achieve rotation invariance, Ojala et al. (Ojala et al. 
2002) presented the term of ‘Uniform’, whose measure 
corresponds to the number of spatial transitions (bitwise 0/1 
changes) in the patterns. However, the ‘uniform’ pattern is 
defined in the case of regular textures i.e. Brodatz’s textures, 
which consist of the vast majority of “uniform” patterns of all 3 
x 3 patterns and it is not the case of satellite imagery through 
our experiment. So we present a rotation invariant LBP form 
LBPp R that is more suitable for describing natural scenes: 
LBPj„ = IX ! ROR(LBP c , i) I i = 0, 1, • • •, P-1} 
P i=0 
(3) 
Where the ROR is defined as: 
ROR(LBP p R ,i) = 
(4> 
LBP, 
P,R » 
i = 0 
2.2 Texture and spectral distribution 
The texture feature is extracted on gray level images in the most 
of the previous studies. For multi-spectral imagery, it does not 
consider cross-band relations (Hu et al. 2005). Although 
Lucieer et al. (Lucieer et al. 2005) considered the cross-band 
relations by multivariate texture model, the method is too 
complicated. In this paper, the texture feature of an image 
region is evaluated by the joint distribution, i.e. a discrete two- 
dimensional histogram, of LBP operator operated on two PCs of 
the image region. In the following experiments, we apply 
LBP S " to calculate the texture distribution LBP ] / LBP\ of an 
image region and compare their efficiency in colour image 
segmentation. The spectral feature of an image region is just the 
joint distribution of grayscale values of its two PCs. As the 
number of bins used in the quantization of the feature space is a 
trade-off between the discriminative power and the stability of 
the feature transform, we set the bins of spectral distribution as 
32 by 32 in the following study. 
2.3 Similarity measure 
In the split and merge segmentation process, we choose a non- 
parametric statistic the G-statistic as a pseudo-metric for 
comparing the similarity between texture and spectral 
distributions. The similarity between a sample and model 
histograms is computed by the formula: 
By our experiments, In the split and merge segmentation 
process, we choose a non-parametric statistic the G-statistic as a 
pseudo-metric for comparing the similarity between two 
histograms and the similarity between two regions i and j is 
measured by weighted sum G-statistic WG(i,j) of the 
similarity measures of three features. Then the similarity 
between two regions i and j is measured by weighted sum G-