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Mapping without the sun
Zhang, Jixian

ZHU Hong-chun®®, ZHANG Ji-xian®, LI Hai-tao®, YANG Jing-hui®, LIU Hai-ying®
©Chinese Academy of Surveying and Mapping, Beijing 100039, China; (2)Geo-information Science & Engineering College,
Shandong University of Science and Technology, Qingdao 266510, China; ©The Department of Basic Courses, Shandong
University of Science and Technology, Qingdao 266510, China
KEY WORDS: region growth, image segmentation, gridding, parallelism
Region growth is an important method based on regional information, and its gridding process also is an important problem. This
paper starts with basic principle and mathematical models of region growth. Then some new serial region growth algorithms
proposed in recent years are classified and analyzed, based on which their related gridding strategies are emphatically discussed.
Then we analyze and put forward the algorithm, which should be feasible when designing gridding parallel segmentation, and give
an evaluation of the parallel regional segment algorithm. Finally, we point out the problems and challenges of future research.
Remote sensing image segmentation is the first step towards
image analysis and application, and it’s the important
components of image understanding. The so-called remote
sensing image segmentation, based on the objectives and
background of a priori knowledge, of the mean is marked target
image and positioned background, and it is said to be physically
meaningful regional connectivity collection of remote sensing
for further lay the foundation for image classification.
Segmentation final result of the image is decomposed into a
number of characteristics with the minimum components. There
are many major traditional segmentation algorithms such as
regional and border detection etc. Faced with geometrically
growth of remote sensing data and rapid treatment needs, a
more efficient algorithm and strategy for image segmentation is
cried for.
Grid has become the third generation of the Internet, promoting
a higher level of resource sharing. In grid environment, all
kinds of special scientific computing and processing can get
maximize the efficiency and speed. Therefore, the study for the
development of Grid remote sensing image processing method
is a practical significance of the issue. This study focuses on the
regional growth of remote sensing image segmentation methods
of Grid strategy and algorithm, and evaluation of the capability
is put up based experimental.
2.1 Regional growth definition
Regional growth is an image segmentation method that is very
concerned about by the Computer Vision, and it is particularly
suited to image texture segmentation. The basic idea is to form
a region through together similar pixels. Input image is often
firstly to be divided into a number similar region, and then
selected a seed and iterative followed with seed pixels around
similar pixels according to some criterion. One its’ focus is the
first seed pixels choice and the growth of design rules the other
is the efficient and accurate algorithm.
2.2 Regional growth algorithm
Step 1: Through image scanning, the seed is identified to
regional growing;
Step 2: Seed pixels’ gray or band characteristics compare with
other surrounding pixels’ (four or eight neighbourhood). If meet
the established criteria for the judgment, then fused as a region;
Step 3: For newly merged pixels, repeatedly step on the
Step 4: Repeated two, three steps, not until the regional
Step 5: Returning to the step 1, and searching new growth point
in the region of pixels.
In general growth algorithm, there are two key points. One is
the seed selection the other is the formulation of guidelines for
growth. Seed pixels selections mainly include the following
(1) Using iterative methods to select the smallest contraction
seed, the shortcomings of this method is the calculation of the
additional burden;
(2) Because the targets mostly have the more larger radiation,
so we can choose the most image-pixel as seed.
(3) If there is no priori knowledge of division, we can calculate
each cluster using the growth criteria. And if the calculation
results showed clustering, the pixel that close to the clustering
center is chosen as seed pixels.
The main criteria for growth based on the following
(4) Through Comparing seed gray difference, the difference is
seemed as a gray Threshold. Identified the threshold method is
based on the mean tonsure or the concavity based on the gray
histogram analysis.
(5) The cumulative histogram can be used, which determining
regional integration using the rule that gray distribution is
(6) The shape characteristics of the region Segmentation can be
use as the decision to terminate the growth conditions, but also
as a regional merger conditions.