224
Index
Methods
Band
PCA
Multiplicative
Brovey
ISVR
SSVR
2 Green
4.7319
5.2813
5.7992
5.3906
5.6933
Entropy
3 Red
5.2046
5.5060
6.0852
5.9295
6.1714
4 Near IR
6.1444
5.7775
6.1940
6.1071
6.3436
Correlative
2 Green
0.655098
0.750773
0.494990
0.883070
0.884670
Coefficient
3 Red
0.642385
0.816172
0.810196
0.917164
0.926713
4 Near IR
0.829165
0.808885
0.910622
0.924181
0.937237
Table 2 Comparisons of different fusion methods
As incapability of comparison of different resolution of fused
image and the image before fusion, the multi-spectral image
and the pan image are firstly degraded to 60m and 30m
respectively, which makes the fusion resolution of the result
30m, then the fusion is carried up, finally the we could get the
result of comparison of the fusion image and the original image,
so are the entropy and coefficient, seen in Table 2.
From the aspect of result of data fusion, the image information
get from the SSVR is the largest. According to the two
indicators, SSVR algorithm not only enlarge the quantity of
information of multi-spectral data, but also it protects the
spectral information contain in the original image. It could
draw an conclusion that SSVR is suit for the advanced image
process such as classification.
5. CONCLUSION AND DISSCUSION
In this paper, a Simplified Synthetic Variable Ratio (SSVR)
fusion method is presented to merge high spatial resolution
panchromatic (Pan) image and high spectral resolution
multispectral (MS) images based on a simulation of the
panchromatic image from the multispectral bands. Landsat7
ETM + images were used to assess the effectiveness of
classification-oriented SSVR method in comparison to
Principal Component Analysis, multiplicative, and Brovey
transform methods.
Compared to other fusion methods, the images generated by
SSVR method have more information and high spatial
resolution while maintaining the basic spectral characteristic of
the original multispectral image, and SSVR method is simpler
to carry out than other SVR methods. The SSVR method is a
good fusion method since its result achieves a good spectral
quality and easy to carry out. In recent studies, there is no
unified fusion standard evaluation effect, which is an urgent
problem to be solved. The shortage is that the method is
rigorous to remote sensing data because it achieves a good
result only when Pan Images has almost the same spectrum
range with MS images.
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