The accuracy of the block-processing image registration method depends on the block size and the number of features extracted from the block. In literature [
15,
16,
17], the effect of the number of features was analyzed in terms of registration error and accuracy, in which a larger number of features was found to provide better results. In order to determine optimal block size (OBS), whole image processed features (WF) are compared with block processed features (BF). Even the number of WF is similar to BF, the accuracy of the registration results may differ, because the use of block processing helps to reduce false matching features with geometrical constraint.
Figure 2 shows the comparison of matching features between non-block processing and block-based processing. From the figure, we can observe that the block-based method provides more accurate matching features, whereas a non-block processing method has false matching features (red square box). In the proposed approach, if the BF is greater than WF, then this block size is considered as the optimal block size. Otherwise, the block size is increased by adding square blocks (SB). In our case, we set
.
The process is iterated until the criterion is fulfilled with the maximum block size constraint. The maximum block size constraint is determined by entropy, which is defined as:
where
p is the frequency of the grey level.
E takes its maximum value when all
p are equal. By this definition, more pixel variation (more information) will have greater entropy. In
Figure 3, the histogram of the grey level values is shown. It can be observed that
Figure 3c has the greatest pixel variation.
Figure 4 shows the entropy along various block sizes. From the figure, we can observe that the
block size has maximum entropy. By this analysis,
is used as the maximum block size for the determination of the optimal block size (OBS). The proposed block-based algorithm is represented in
Figure 5. Through various experiments, we determine the optimal block size to be
. In order to extract the features, the panchromatic reference image
and the multi-spectral sensed image
are used. Then, matching points are selected by the Euclidean distance
between descriptors.
where
,
are the first and the second descriptors, respectively.
Figure 2.
Matching features comparison: (a) non-block processing; (b) block processing.
Figure 3.
Histogram of each block size: (a) ; (b) ; (c) .
Figure 4.
Entropy plot for various block sizes.
Figure 5.
Flowchart of the block-based algorithm.