Image Processing Reference
In-Depth Information
Table 9.2 Performance measures for various techniques for visualization of the geological data
( c
2011 Elsevier)
Fusion technique
Relative bias
Fusion factor
Fusion symmetry
Bilateral filtering technique
Three band selection
Piecewise linear function
Color matching function
MRA technique
For a comparison of the numerical values, we provide the results of the participa-
tory performance measures for the final images F , using all these studied techniques
for theMoffett data Table 9.1 , and the Geological data Table 9.2 . The multi-resolution
analysis (MRA)-based technique turns out to be superior to most of the other tech-
niques in terms of relative bias b and fusion symmetry FS. However, a very low value
of the fusion factor FF indicates a smaller amount of mutual information between
the final image and the constituent bands. Therefore, although the participation from
the image bands is quite uniform, the quantitative gain from the bands in terms of
information is less as indicated by low values of the fusion factor.
The bilateral filtering-based technique performs well in terms of low values of
the relative bias and fusion symmetry, indicating that the final image has a lower
derivation from the constituent images in terms of intensities, as well as information
content. High values of fusion factor FF indicate a significant amount of mutual
information between the constituent bands and the fused image.
The techniques using piecewise linear functions and color matching functions
provide results with a high variance yielding a good contrast in the final results
(will be discussed in the next chapter). The objective assessment shows that both
techniques offer a high value of fusion factor (FF), but values of the fusion symmetry
FSare also large, which is not desirable. This indicates an uneven participation from
input hyperspectral bands. Thus, not all the bands contribute uniformly toward the
fusion process. Additionally, the values of the fusion symmetry parameter for these
two techniques were found to vary over the test datasets as well. The technique of
piecewise linear function and the color matching function assign a constant fusion
weight to each of the constituent bands. In case of the piecewise linear function
technique, these fixed weights are derived from the optimization of certain criterion
in the sRGB space, while the technique of color matching function assigns these
constant weights to the particular band by modeling certain perceptual properties of
the human eye. In both cases, however, the calculation of weights is fixed and does
not consider the local variation in data contents at all. Quite naturally, the subset of
bands receiving higher weights has a higher contribution towards the fused image.
Therefore, features from this subset of bands find a good representation in the fused
image. On the other hand, features in rest of the bands are likely to contribute very
little as the corresponding bands have smaller fusion weights. Therefore, one can
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