Image Processing Reference
In-Depth Information
from a set of pre-defined quality measures of the input data. We calculate the sensor
selectivity factor
β
k
(
,
)
(
,
)
in a particular band
k
as the
product of different individual quality measures of the image. If
Q
1
,
x
y
for a particular pixel
x
y
Q
2
,
···
,
Q
n
are the different quality measures, then the value of
β
is given by Eq. (
5.5
).
n
Q
j
I
k
(
)
,
β
k
(
x
,
y
)
=
x
,
y
(5.5)
j
=
1
where
Q
j
(.)
indicates evaluation of the
j
-th quality measure. These quality mea-
sures, however, should satisfy certain properties. Though we are not dealing with
the conventional fusion weights, the non-negativity of the sensor selectivity is an
essential condition. This factor reflects the fractional component of the true scene
captured by the given sensor element. Intuitively, and by definition, this compo-
nent has to be non-negative. The minimum value of
can be zero, which indicates
pure noisy data without any relation to the true scene. The quality measure should,
therefore, produce a non-negative value, and it should also be confined to a finite
range. If the quality measure produces a negative value due to reasons such as small
magnitude, phase reversal, etc., the multiplicative combination of measures given
in Eq. (
5.5
) may as well turn out to be negative, and change the notion of the cor-
responding measure. Non-negative values enable a direct combination of multiple
quantities without a need for sign change. The value of
β
is calculated as the product
of the local quality measures, and hence, even if a single measure has the value of
zero, the corresponding
β
will be enforced to be zero producing erroneous
results. Secondly, these measures should be confined to a finite range. We do not need
a normalization across all the bands of hyperspectral image, as typically done for the
conventional fusion weights. However, the
β
k
(
x
,
y
)
β
values should be confined to a specific
range. The designated range for
is [0, 1], however the actual value of products of
chosen quality measures may not confine to this range. One has to appropriately scale
the computed product of quality measures to the designated [0, 1] range. We have to
compute these quality measures over each pixel in each spectral band of the hyper-
spectral data. Therefore, it is desirable to select computationally efficient quality
measures
Q
j
's.
Out of the various existing no-reference qualitymeasures, we choose the following
two:
β
•
well-exposedness, and
•
sharpness.
The gain of the hyperspectral sensor array as a function of the wavelength varies
greatly over the entire span of the bandwidth of spectral bands. Pixels with very high
values of intensity appear too bright due to over-saturation. On the other extreme,
poorly-exposed or under-saturated pixels appear too dark, and thus, fail to convey
visually useful information. As these pixels do not provide any significant infor-
mation, one naturally expects lesser contribution from such pixels towards the final
fused image. The sensor selectivity factor for such pixels should obviously be small.
