Image Processing Reference

In-Depth Information

hierarchical implementation. However, the successive bands in the hyperspectral data

exhibit a high degree of similarity. When the randomly arranged bands are grouped

for the first level of hierarchical implementation, the corresponding fusion results

contain integration of highly dissimilar information as compared to the sequential

case.

The advantages of the hierarchical scheme are as follows:

•

This scheme reads only a fraction of the hyperspectral image bands at a given

time to generate an intermediate fusion result. The hierarchical scheme requires

only a small subset of the input to be read in the memory and process it. Thus, the

memory requirement is significantly reduced.

•

It enables the user to scale up the system to efficiently fuse any number of images.

It can easily accommodate any increase in the number of bands without compro-

mising with the performance.

•

The system is computationally efficient, and makes it possible to parallelize the

implementation. All the subsets of the hyperspectral image can be processed in

parallel to produce a set of intermediate fusion results.

•

The resultant images at the intermediate stages facilitate analysis and visualization

of midband reflectance response of the scene. The fused images at the first-stage

represent the response of the scene over a bandwidth that is
M
times that of an

individual hyperspectral band. These and the subsequent intermediate results can

be used to visualize the combined response of the scene over a range of bandwidth

encompassed by the number of bands being fused.

3.6 Implementation

The fusion procedure requires selection of three parameters-

σ
R
, and
C
.The

choice of these parameters is important to achieve a better quality of the output.

The implementation of the bilateral filter is also an important parameter as far as

the computational complexity and timing requirements of the entire procedure are

concerned. Here we use the implementation based on the approximation of bilateral

filter provided in [126]. In order to automate the fusion algorithm without much

degradation in quality, we have adopted the guidelines suggested in [126] to select

the values of first two parameters.

σ
S
,

σ
S
=

C
1
×

min

(

X

,

Y

)

(3.7)

C
2
×
max

))

σ
R
k

=

(

I
k
(

x

,

y

))
−

min

(

I
k
(

x

,

y

∀

k

,

(3.8)

where
C
1
and
C
2
are positive real numbers which can be set to obtain the desired

quality output. The choice of
C
1
is related to the size of spatial details retained during

fusion. We have used
C
1
=

16 in all test experiments. The value of the range kernel

defines the minimum amplitude that can be considered as an
edge
.Wehaveset
C
2

to 0.05 in our experiments.

1

/