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Mapping without the sun
Zhang, Jixian

the high-resolution remote sensing images in time T2 and land
use map in time Tl, Development of image processing and
interpretation techniques, automatic extraction the land use
change, automatic updating land use maps, and so on, Has been
a main research topic of remote sensing technology.
The new methodology of LUCC change detection based on the
land use segments other then the pixels. In detail, firstly, by
means of the sample segments according to land use map in
time Tl, the features of each type of the land use classes are
obtained in time Tl. Secondly, each samples are analysed, if
the index of regional similarity between the image segments of
Tl and T2 is accepted, the samples in time T2 could be
remained, otherwise the new samples around that sample are
selected and are judged by the similarity between the samples
of Tl. Thirdly, each segments of T2 will be classified
according to the minimum Euclid distance to the T2 sample
segments accepted, and the corresponding land use type will be
assigned to the current segment.
2.2 Methodology and work procedures
In general, the method has three components:
Based on the features of the area (A) and perimeter (P) of the
i k = ^
land use segment, the shape index K is defined as :
2.2.2 The construction of the change index
When the RS image has been conflated with the land use map
in time Tl, the preceding characters of each type of the land use
classes are extracted based on the area of land use segment.
After that, the characters of each segment are calculated on the
image in time T2, we can evaluated the similarity of the region
inside each land use segment at different time.
In this study, we put forward two types of indicators of
evaluation of the regional similarity, they are : Index of regional
similarity (T'V) as well as ratio of characters’ difference( a tj ).
T t j is defined as:
1 ( X ik X jk )
mh S7
2.2.1 Feature extraction and expression based on the
LUCC map
1) Grey character
There are two kinds of statistical analysis for multi-spectral
remote sensing data : modular statistics and multi- statistics.
Traditional statistics includes: Mode, Median, Mean, Range,
Variance, Standard Deviation, etc.
Here, Mean , Maximum, Minimum and Standard Deviation was
chosen for characters for spectra character.
2) Texture character
Texture features is one of the most important features of images.
Texture classification is the basic problem of image processing.
There are lots of methods for texture analysis, the following
types can be summed up: statistical approaches, Structure
approaches, random approaches and so on. At present, the
traditional method is the statistical method in remote sensing
image texture analysis .
where V- : regional similarity of each characters in i
segment(Tl) andj segment(T2)
X ik : character K of segment i
Xj k : character K of segment j
171 : numbers of characters
S k : standard deviation of the character k
s k =
\n-\ M
n : numbers of segment each land use classes
k = (1,2,...m)
171 : numbers of characters
Due to diversity of dimension among three kind of characters,
we replace X jk and X jk with x' ik and x'- k .
In statistical approaches, the gray level cooccurrence matrix
(GLCM) is used abroad and thought of as most effective texture
features. In this study, we calculated four 8-gray levels
cooccurrence matrix with the neighbor pixels displacement
belonging to {(1, 0), (0, 1), (1, 1), (1, -1)}. We haven’t
considered the rotation-invariant characteristic of texture
features in our experiments. We extract four statistics from four
GLCM: entropy (ENT), contrasts (CON), angular second
moment (ASM) and inverse difference moment (IDM), with
their definitions refer to [15]. The major drawback to this type
of method is long computation time.
3) Shape character
The size and shape of the object is one of the important features
for the object recognition, although spectral feature can
describe the majority information of the object. But for some
classes (such as : irrigated fields and orchards, rural settlements
and independent mining sites) spectral values are very similar,
but differ in shape. Therefore, for some special classes we need
to extract the shape features.
1 «
X, =-£*a
» ¡-1
fly is defined as :
a H =
X ik + X jk
where x' ik and x' jk are same to equation 3
2.2.3 Change detection based on the land use segment
Each sample are analysed, if the index of regional similarity
between the image segments of Tl and T2 is accepted, the
samples in time T2 could be remained, otherwise the new