248
2.3 Bands combination
Multi-spectral remote sensing data has many bands, how to
make bands combination is the key. Band chosen should adhere
to the following three princilies[3-7].l) selected the three bands
to large volume of information; 2) selected the three bands
band combinations with other features has a good separability.
• information of band combination (within the coverage of all
the features of the total amount of radiation) the largest of the
Satellite
Year
Frame
Time
Bands
Landsat 185*185 (MSS 80)
1976
128/31
09.16
4 bands
Landsat 185*185 (MSS 80)
1981
128/31
10.14
4 bands
Landsat 185*185 (TM 30)
1995
119/32
05.15
7 bands
Landsat 185*185 (ETM)
1996
120/31
05.24
7 bands
Landsat 185*185 (ETM)
1996
120/32
05.24
7 bands
Landsat 185*185 (TM 30)
2001
119/31
08.11
13 bands
Landsat 185*185 (TM 30)
2001
119/32
08.11
9 bands
Landsat 185*185 (ETM)
2001
119/32
09.03
9 bands
QUICKBIRD16.5*16.5(PAN 0.61)
2005
10.20
3 bands
CEBERS 113*113 (CCD 19.5)
2005
368/53
05.18
5 bands
Table. 1-1 remote sensing data source
Land type
No.
Meaning
Water
1
Include water, regional water, rivers, reservoirs, ponds.
Residential
land
2
Residential sites and mining sites, land transport.
Dry land
3
Main planting com, soybeans, and other crops
Water land
4
Main rice planting
Sink land
5
Mining Subsidence
Table.2-1 system of mine land change classification
Before
classification
After classification
Water
Construction
Dry land
Water land
Sink land
Water
238
1
0
0
1
Residential land
2
512
14
4
7
Dry land
0
3
603
8
1
Water land
6
11
5
420
6
Sink land
8
6
7
5
170
Sum
254
533
629
437
185
Percentage
93.7%
96.06%
95.87%
96.11%
91.89%
Table .2-2 classification confusion matrix of study area
three-band (single wave of the standard deviation, correlation
coefficient between the band and identify common index OIF),
Together with the features of different spectral characteristics,
determine the best band combinations.
2.4 Supervised classification of remote sensing images
Based on the district tiered
The existing image classification methods are generally the
same standard of the whole image classification, However,
remote sensing images spectrum feature are more complicated,
even after treatment, the images are often arise from different
spectrum, foreign body with the spectrum, Hill shadow effects
and significantly different regions or different seasonal
characteristics of the images are quite different phenomena
Therefore, the district is closer to the idea of remote sensing
image characteristics, inline with the interpretation of ideas.
1 n Mine resources and environmental monitoring
classification system
2-, Supervised classification of mine district remote sensing
image
First, chose classification samples according with the
classification system of mine district, in the process of
selection as far as possible to ensure accuracy of the sample.
After obtaining samples of repeated screening and finally
obtain the classification training template. We choose sample
of the 1995 TM image of mine district, obtain the region’s
classification confusion matrix, the table below.
From Table 2-2, we can see that the classification of the
various types of samples before the individual classification
after most still belong to their original category. Only a few
