Image Processing Reference
In-Depth Information
Table 10.7
Performance measures for various techniques for visualization of the urban data
Fusion technique
Variance Entropy Avg gradient Relative Fusion
Fusion
bias
b
σ
2
H
g
¯
factor
FF
symmetry
FS
Bilateral filtering technique
619.50
6.28
6.68
0.17
1.49
0.11
Bayesian technique
855.56
6.34
7.16
0.21
2.04
0.16
Variational technique
558.26
6.14
6.57
0.13
1.58
0.26
Optimization-based technique 665.11
6.43
7.46
0.24
1.47
0.14
Three band selection
477.04
6.41
6.13
0.25
1.33
0.12
Piecewise linear function
282.54
5.80
4.55
0.31
1.34
0.22
Color matching function
260.90
5.78
4.59
0.22
1.20
0.23
fusion process involves significant computation at every iteration, it is possible to
speed up the computation to some extent by pre-computing the data-dependent terms
that remain unchanged throughout the fusion procedure. The average time for a single
iteration for the AVIRIS data was 72 s, while the time required for the single iteration
of the Hyperion images was found to be 28 s. Nearly 9-10 iterations were required
for the solutions to converge.
The optimization-based solution too, is an iterative solution where the fusion
weights are re-computed at every iteration. This solution, thus, turns out to be compu-
tationally expensive. Additionally, each iteration involves several terms that require
a dot product and an element-wise product. On average a single iteration took 92 s
for the AVIRIS datasets of dimensions
(
614
×
512
×
224
)
pixels, while the Hyperion
datasets with dimensions
pixels produced the result of a single
iteration in 35 s. It took nearly 6-8 iterations for all the datasets till the solutions
converge. This algorithm, however, has a vector operation defined over the spec-
tral arrays (
s
(
512
×
256
×
242
)
). Each dataset contains
XY
spectral arrays which are processed
independently to generate the pixels of the fused image. We believe that these inde-
pendent operations can be parallelized to a large extent so that the computation time
can be drastically reduced.
The band selection technique chooses the three bands through several filtering
operations, and thus turns out to be quite fast. The PLF and the CMF techniques assign
a fixed weight to each of the spectral bands, and thus the process of fusion reduces to
merely a weighted linear addition where weights have already been predefined. On
average, these techniques generated fused images from each of the AVIRIS datasets
in 29, 12, and 11 s, respectively. For the Hyperion datasets, the average computation
times required to generate the fused images for these techniques were 12, 7, and
7 s, respectively. The details of the computation time for these techniques have been
summarized in Table
10.8
.
(
x
,
y
)
