The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
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al.,2007). This way, the original multispectral image can then
be used as a reference for evaluation.
Methods
Quality measures
SAM
ERGAS
Q4
GIHS
2.26
6.15
0.53
Criteria-based
0.98
5.84
0.57
Table 1. SAM, ERGAS and Q4 values of the test methods
Table 1 lists the values of the three quality measures for the
GIHS transform and criteria-based image fusion methods. The
criteria-based image fusion method has a constraint (Eq. 9) to
keep the ratio of the multispectral bands after fusion. Therefore,
it has good SAM scores that are smaller than I o . On the other
hand, the SAM value in Table 1 suggests that the GIHS method
would cause certain angular (2.3° ) or colour distortion, which
is consistent with visual evaluation on Figure 2.
As seen from Figure 2 and Figure 3, the fusion results from the
GIHS transform and the criteria-based method are mostly
comparable. This is shown by the very similar quality measure
values in ERGAS and Q4, where the criteria-based approach
shows slightly superior properties. When both spectral and
spatial qualities of the fused images are considered, the criteria-
based approach provides the adjustability to balance between
these two considerations. These results imply that the criteria-
based method can be an alternative to the popular colour-based
methods since it has good spectral and spatial performance.
Finally, it should be pointed out that both the GIHS transform
method and the criteria-based approach can accommodate the
fusion of multiple (>3) number of bands.
5. CONCLUSIONS
Two image fusion approaches have been developed and
implemented. It is shown the classical IHS transform can be
generalized to multiple dimensions such that image fusion can
be performed with any number of bands under the concept of
IHS transform. Furthermore, the generalized IHS transform is
interpreted in terms of wavelet transform. It is shown that this
transform is equivalent to a wavelet transform in spectral
domain, where the first component is the intensity or band
average, and the other components are band differences relative
to band averages calculated in a sequential combination of the
involved bands, all up to a constant. Tests demonstrate that the
generalized IHS transform can produce stable and superior
fusion results than the classical IHS transform approach.
The criteria-based image fusion method forms the fused images
as the linear combination of the input panchromatic and
multispectral images. Three criteria are introduced to determine
the weighting coefficients which determine the contributions of
the panchromatic and the multispectral images to construct the
fused pixels. In the criteria-based fusion method, quality and
properties of the fusion results are known since the results are
obtained based on pre-defined criteria. This can be treated as a
novel framework for image fusion, where users can design their
own fusion tools based on their needs. In addition, the linear
combination model provides the flexibility to balance the
spatial quality and spectral quality in the final fusion outcome
such that an optimal fusion result can be achieved. Tests results
along with visual and quantitative evaluations demonstrated that
satisfactory fusion results can be obtained with the criteria-
based method, whose performance is comparable and in general
superior to colour-based ones, including the GIHS.
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