Initialization for Image Registration using Feature Matching
Similarity Assessment
We use root mean square difference (RMSD) to measure the similarity for FD and IM. For a
sensed image and its counterpart, i.e., reference image, the RMSD for FD is defined as
br>
Two shapes are with higher similarity when smaller RMSD is observed.
The RMSD for seven IMs is defined as
For SM, referring to figure 1, the similarity between shape A and B is calculated as
Figure 1. Illustration of SimilarityAssessment for SM
The index P(A,B) reflects higher similarity when higher value is calculated.
Matching
Considering three indices, i.e., RMSD
FD, RMSD
IM, and P(A,B), a successful matching should
fulfill following criteria:
(1) RMSD
FD<=
dFD
(2) RMSD
IM<=
dIM
(3) Min(RMSD
FDxRMSD
IM
(4)P(A,B)<=
dP(AB),Max(P(A,B))
(5)A subset of the intersection from the three sets that fulfills the four criteria is selected as the potential matching pair.]
Experimental Results
The test area is located in north Taiwan. The reference image is an orthorectified aerial photo
with 2000x2000 pixels at 1.12m pixel spacing as shown in figure 2(a). The sensed image is an
airborne scanner image with 512x512 pixels at 4.5 nominal ground resolution as shown in figure
2(b). The sensed image was resampled to 2000x2000 pixels for processing convenience. The
preprocessed images, including the procedure of AS, SNN and edge enhancement, are shown in
figure 3. The segmented blocks using Energy method are shown in figure 4.
(a) |
(b) |
Figure 2. Test Images (a) Reference Image (b) Sensed Image
|
(a) | (b) |
Figure 3.Preprocessed Images (a) Reference Image (b) Sensed Image
|
By visual inspection, the corresponding polygon pairs are shown in table 1. Considering the
combined results of FD and IM, table 2 shows the correspondence. The correspondence using
SM only is illustrated in table 3. Combining FD, IM, and SM, the matching results are shown in
table 4. The final results, with robust estimation, are shown in table 5.
