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Mapping without the sun
Zhang, Jixian

Song Weidong 3 , Wang Jingxue 3 *, Qin Yong b
J School of Geomatics, Liaoning Technical University, Fuxin, Liaoning, 123000, China—xiaoxuel861@163.com,
song_wd@ 163 .net
b Surveying Engineering Department, Shandong University of Technology, Zibo, Shandong, 255049, China
Commission VI, WG VIM
KEY WORDS: Sole period RS Image, Spot, Maximum Likelihood Classification, Spot Class Statistic and Judge Function, Change
Detection in Land Use
Depending on the land use vector data, this paper discusses one method of land use changing detection based on the sole period RS
image. Firstly, this paper overlays the present land use map and RS image in space, then carries out the category attribute judgment
in the land use spot units according to Maximum Likelihood Classification results. In this step, this paper proposes the Spot Class
Statistic and Judge Function. And then this paper matches the land use spot units with the classification results, marks the changed
spots in high-light, and calculates those areas. Finally, this paper carries the precision analysis and evaluation on detecting results
according to the changed spots areas. The experiment results indicate that this method can obtain a preferable detecting effect.
Land use is rational or not, not only directly relates to the
quality of human's life, the social environment and the
ecological environment, but also decides whether the land use
sustainable developmental strategy is feasible. Understanding
the reasons, the process and the future development tendency
of land use changing, will be the center for various countries in
the research of land use/land cover.
Through the investigating to land use changing situation of
some regions, and gaining the land use changing information in
certain time, are advantageous to prompt accurately grasp land
use changing, understand and analyze the present land use
situation and the change rules of these regions, then serve for
constituting reasonable land use and regulatory policies; It
has the extremely important significance to promptly
understand the region economy development condition by
studying the space-time distribution, the quantity, the
characteristic, the dynamic change process and the
development tendency of the land use.
The methods of land use change detection mainly can be
divided into two kinds: one is to use the different period RS
images to detect the change information, and the other is to use
the sole period RS image and the present land use map to detect
the change information.
At present, most of areas do not have multi-period RS image,
only have the sole period RS image. According to this general
situation, the paper discusses a remote sensing change detection
method, which is based on the land use vector graph. This
paper utilizes the Maximum Likelihood Classification into the
sole period SPOT5 multi-spectral image, and compares the
classification results with the land use vector graph, we could
detect change information.
2.1 Maximum Likelihood Judgment Rule
The Maximum Likelihood Classification utilizes the statistic
features of RS data, and regards the distribution of RS
multi-band data as multi-dimension normal distribution to set
up classification judgment function, then obtains the
classification results. In the viewpoint of probability and
statistic, if want to judge the vector X in certain position will
belong to which kind of categories, it should be decided by
judgment function:
g t № = % /X) = P(X/w k )P(w k )
k = (1,2,3,•••,/«) (l)
In this formula, W k represents the k category, P(w t /X)
represents the probability of X belongs to W k , which is called
post-examination probability, P(\V k ) represents the
pre-examination probability, which is the probability of X
existing in W k . In this research, this paper doesn’t consider
the instance of different categories mixed in one pixel, and
regards pixel X only belonging to one given category. Because
this given category is unknown, this research should calculate
the all probabilities of X belonging to each category, and
compare those magnitudes, then divide this pixel to the
category having the biggest probability. The judgment rule is:
P(w k IX)>P(w ¡ IX) (2)
k * j, j = (1,2,3, — ,m)
formula is existing,
X ew k
2.2 Multi-variable Probability Density Function
The known pixels belonging to each category constitute some
certain point clusters in plane or space. Each unidimensional
data of each category forms one normal distribution in its own
number axis, and the multi-dimension data of this category
forms one multi-dimension normal distribution. As the RS data
usually represent multi-variable normal distribution, it makes