Image Processing Reference
In-Depth Information
Table 10.6
Performance measures for various techniques for visualization of the coral data
Fusion technique
Variance Entropy Avg gradient Relative Fusion
Fusion
bias
b
σ
2
H
g
¯
factor
FF
symmetry
FS
Bilateral filtering technique
1195.82
6.98
6.97
0.21
1.36
0.23
Bayesian technique
1435.03
7.09
7.07
0.26
1.35
0.52
Variational technique
1087.80
6.91
6.73
0.18
1.43
0.51
Optimization-based technique
984.32
6.82
6.87
0.24
1.65
0.61
Three band selection
884.82
6.86
6.71
0.79
0.38
0.14
Piecewise linear function
871.78
6.75
5.83
0.25
1.40
0.42
Color matching function
534.30
6.00
4.70
0.24
1.34
0.19
The details of the quantitative assessment of the results of the urban data are
provided in Table
10.7
. We may also observe high values of the variance and the
average gradient for all the presented techniques as against the corresponding values
for the other techniques described here except possibly for the variational technique.
The smoothness constraint in this case helps producing smooth and thus visually
pleasing images, but at the cost of reduced sharpness. The contributions from the
input bands are also quite high for all the discussed techniques as observed from
the fusion factor FF. Fusion using the PLF and the CMF have captured some of
the subtle features from the data, however, their results appear quite dull, and thus
lack visual appeal and the clarity of details. As per Table
10.7
, all techniques have
performed well in terms of low values of the relative bias
b
with the variational
technique providing the smallest value indicating the least deviation from the input
data with respect to the mean intensity value. Also, low values of fusion symmetry
FS indicate high level of uniformity in the participation from various hyperspectral
bands towards the fusion output for all techniques studied in this chapter.
We now discuss the computational requirements of these fusion techniques before
we conclude this chapter. We have implemented all the algorithms in
®
version R2009a running on a P-IV computer running at 2.66 GHz with 2 GB RAM.
Out of four techniques discussed in the monograph, the bilateral filtering-based one
is a non-iterative single pass procedure. The technique initially produces a bilaterally
filtered output of every band in the data. These outputs are used to compute the fusion
weights while the actual fused image is produced via a normalized sum of bands.
We have used the fast implementation of bilateral filter as described in [126]. The
moffett
2
,moffett
3
, and lunar datasets provided by the AVIRIS contain 224 bands
of dimensions
Matlab
pixels each. For the aforementioned computer system,
the bilateral filtering-based solution took nearly 120 s to generate the fused image
geological, coral, and urban datasets contain 242 bands of the size
(
614
×
512
)
(
×
)
pixels
each, provided by the Hyperion. The proposed solution took nearly 27 s to generate
the fused image with 3 stages of hierarchical processing.
The Bayesian technique can be split into two parts. The first part deals with
the computation of the sensor selectivity factors
512
256
β
over every pixel in the data.
