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Title
Mapping without the sun
Author
Zhang, Jixian

249
other individuals assigned to the category, the correct
identification rate is 94.726%. This shows that the selection
and screening of the training samples have high rate of correct
identification. They used for classification will be a good
separation effects. The correct identification rate which is
calculated by confusion matrix is an indirect measure of
officially supervised and supervised classification mapping.
Adopt of this approach, based on our land surface
characteristics of the mining area classification system, the
district stratification of 1981 respectively. 1995, 2001, remote
sensing images for supervised classification, and reached a
basic classification accuracy, ensure the correct classification
rate of 94%.
3 ANALYSIS OF LAND SURFACE
EVOLUTION THROUGH MULTI-PHASE REMOTE
SENSING IMAGE
There are two ways that Satellite remote sensing images will be
used in regional land surface dynamics of the evolution: One
is directly based on remote sensing imagery, through an
integrated multi-temporal image analysis and evaluation;
another is the use of information extraction, classification
results in GIS support, combining spatial analysis model
analysis, which is Classification comparison. In this study we
mainly uses the above two methods of the study area which
were monitored for the analysis.
visnsdmqmos mo rì ■„
3.1Remote sensing Monitoring and analysis based on image
•jrif oi ¿fumndt 'jit
^Tmies
Land type
1976-1981
1981-1995
1995-2001
2001-2005
Water
-3.4
-8.5
-13.5
3
Residential
land
4.4
11.8
18.4
2.7
Dry land
5.3
14.4
7.5
2.2
Water land
-5.7
-14.6
-9.9
-4.3
Road
-0.6
-3.1
-2.5
-3.6
Table .3-1 change table of different time type size
■ ■■ !-. >
Area static of every land type of different years
‘jdt 'io bna srii ri-.
□ water land
■ residential
land
□ road
□ dry land
■ water
Fig.3-1 Area static of every land type of different years
Area static of every years of different land type
water land residential land road
land type
dry land
Fig.3-2 Area static of every year of different land type
We used principal component analysis model for the 1995 and
2001 two-phase TM Image Analysis of Evolution, we select
the most informative bandsTM3, TM4, TM5 for each phase,
and make principal component analysis. Make analysis of the
PCA images, find that PCI, PC2 and PC3 covering all image
information. Analysis of the principal components, we can see
that: first and second principal component mainly reflects the
image of the phase of relative stability, which is primarily
used to identify unchanged, the third principal component was
mainly reflects some changes.