3. THE PARALLEL ANALYSIS AND GRID 
STRATEGY OF REGIONAL GROWING 
SEGMENTATION 
3.1 Traditional algorithm limitations 
For regional growth division, several seed selection criteria and 
growth criteria of traditional algorithm have its shortcomings. 
Such as the iterative method and clustering method selected 
seeds add the computational burden, but each growth criteria 
use up a lot of computing resources and can only solve region 
segmentation that satisfy certain conditions. Remote sensing 
images have its characters such as regional, multi-band etc, so 
ideal growth guidelines should consider a variety of criteria for 
the growth of integrated application. To achieve this purpose, a 
stronger and more computing resource is needed, and single 
machine must develop to multi-machine integration. 
Grid features of the impact of remote sensing are enormous. 
Firstly, Grid can solve the massive remote sensing data storage 
and access issues. Secondly, the computation time of some 
large image can be reduced by Using Grid computing resources. 
And then, Software and human resources to the high-sharing 
agreement for the remote sensing images efficient, accurate 
processing is a useful way to achieve. 
3.2 The parallel analysis and Strategy of image 
segmentation 
Grid is an environment an tool including parallel characteristics, 
and it can realize multi-machine parallel processing for remote 
sensing image segmentation. Its feasibility mainly includes the 
following: 
3.2.1 Data parallelism: RS pixels are relative independence, 
and the more senior RS data processing has greater relation. 
Because image segmentation is the preliminary work for image 
classification and feature extraction, so the large volume of data 
in remote sensing image data can parallel compute on the basis 
of block. 
The whole image pixel is ordered by gray level: 
IJ . T_T 
1 rain ' 11 max 
• The F m[n grey pixel is initialized of the first seed. By 
searching through four Neighborhood pixel and stopping 
until f (x, y) < Z , the first segment region is formed. 
• Then according to f > f m \ n + Z , the most recent pixel 
distance from the last region is selected to go on searching 
until /(x, y) < IZ . 
• There are not stop processing repetitively 
until /(x, y) + nZ > F max . 
• The Image loopholes is detected and the pixel value is 
computed by [/(x,/) - (/ min + YlZ)] < Z The 
pixel f (x, /) is classified to the n-recursive segmenting 
region if meeting the conditions. 
• Each preliminary Segmentation region’s basic parameters 
are calculated, such as regional area and regional 
average value . 
• If satisfying the condition < S , the segment region is 
combined with the most neighboring region. 
Repeating the process, image segmentation is completed. 
(3) Some explanations 
In order to avoid over-segmentation problems, we can make use 
of smoothing factor, for example Gaussian smoothing function, 
to preprocess the image in the segmentation process. 
We can utilize the mean gradient method to obtain the 
segmental threshold on the basis of the image pre-processing. 
4. GRID PROCESSING FOR PARALLEL 
IMPLEMENTATION AND PERFORMANCE 
EVALUATION 
3.2.2 Algorithm parallelism: Regardless of what kind of 
growth segmentation criteria, the processes of seed points are 
targeted at the iteration process. So parallel operation is 
possible. 
3.2.3 Segmentation parallel strategy: Various 
segmentation criteria can be integrated use. Based on the 
growth of gray value segmentation results, the test for gray 
structural features and characteristics of the border region 
division can be simultaneously process to achieve better 
segmentation results. 
3.2.4 Parallel algorithm: 
(1) Parameter settings: 
There are many experiential parameters such as Z(Gray 
threshold of Segmentation), S (the minimum segmentation 
region size threshold), D C! M 2 ( a complete image of the 
scope of M x M), j \ D —> M ( / denote the pixel Gray 
value), h max and h mi „(respectively the smallest and the largest 
gray value among/). Recursive process is growth from h min to 
h max gray level and using point f as seed to form division 
followed. 
(2) A step-by-step approach of parallel segmentation 
methodology for RS data is described below: 
In this paper, the regional growth segmentation algorithm was 
put forward, and during conducting of parallel program in the 
grid environment we can make use of functional and data 
hybrid parallel programming model, the concrete realization of 
the algorithm described in the following: 
Parallel function has the basic idea: "Copy Image divided 
parameters", the various functional module handles the data is 
the same. Assuming there are N number of processors and R 
number of the parameters who need for the calculation, 
parameters will be average allocated to the N processors and 
each processor calculate the parameter that distributed, then a 
result sent to the main system processor, and the main processor 
is responsible for the integration of the final results. Parallel 
data has the basic idea: "classified images, reproduction 
parameters", the image is divided into N blocks average. Each 
processor only put up all steps for the correlation coefficients 
calculated. 
The algorithm is a dimensional growth, and the seed choice is a 
process of small to large. Once identified segmentation 
threshold, the seed points sequence of pixels generate 
automatically. Every one related to the growth of regional was 
generate. This process is repetitive, and seed growth process is 
independent of each other. So we can design data parallel 
algorithm. In order to further provide the, Segmentation of the 
region can achieve their own parallel gray regional 
characteristics and supply parameters to further union region. 
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