Image Processing Reference

In-Depth Information

Table 10.3
Performance measures for various techniques for visualization of the moffett
3
data

Fusion technique

Variance

Entropy

Avg gradient

Relative

Fusion

Fusion

bias
b

σ

2

H

g

¯

factor
FF

symmetry
FS

Bilateral filtering technique

374.29

5.60

4.62

0.31

1.75

0.21

Bayesian technique

451.84

5.17

4.82

0.32

1.93

0.94

Variational technique

385.35

5.63

3.53

0.18

1.95

0.26

Optimization technique

527.12

5.71

4.47

0.40

2.03

0.98

Three band selection

363.64

5.24

3.30

0.36

1.29

0.49

Piecewise linear function

282.05

5.55

3.18

0.45

1.47

0.47

Color matching function

157.50

5.19

2.59

0.44

1.24

0.28

Table 10.4
Performance measures for various techniques for visualization of the lunar
2
data

Fusion technique

Variance

Entropy

Avg gradient

Relative

Fusion

Fusion

bias
b

2

σ

H

g

¯

factor
FF

symmetry
FS

Bilateral filtering technique

642.93

6.32

4.59

0.24

1.65

0.08

Bayesian technique

630.91

6.47

4.43

0.19

1.42

0.04

Variational technique

611.25

6.41

4.22

0.14

2.12

0.36

Optimization technique

669.91

6.40

5.02

0.23

2.14

0.15

Three band selection

658.49

6.47

4.35

0.21

1.83

0.11

Piecewise linear function

642.35

6.45

4.61

0.16

1.70

0.04

Color matching function

612.49

6.56

4.47

0.16

2.15

0.06

formed by the specifically selected three bands as shown in Fig.
10.3
e is capable of

capturing most of the data contents. The bilateral filtering-based solution particularly

enhances the weaker features in the data, and therefore, provides somewhat higher

amount of visual information than the other techniques. These minor differences in

the quality of the fusion results are observable from the quantitative performance

measures provided in Table
10.4
. All the techniques generate fused images with sim-

ilar values of variance and entropies. The fusion factor FF and fusion symmetry FS

of these techniques indicate comparable participation of bands for all the techniques.

The result of variational technique possesses the smallest value of the relative bias

by virtue of the iterative correction in the mean of the fused image, which in turn

measures the deviation of the output intensity from that of input bands. Higher value

of the fusion factor for this technique also indicates a large amount of information

transfer from the input bands towards the fusion output.

The optimization-based technique yields the highest value of the average gradient

than any other technique, which results in a better perceived sharpness in Fig.
10.3
d.

This technique processes the hyperspectral data for obtaining an image with high

contrast, irrespective of the input characteristics. Therefore, we are able to obtain a

high value of the variance in the result despite lack of contrast within the input bands.

As we may note, this advantage comes at the cost of a higher value of fusion symmetry.

Having discussed the results of processing the AVIRIS data, we consider the

remaining three hyperspectral datasets provided by the Hyperion imaging sensor.