Difference between Using Multiple Nearest Neighbors and Using Only the

First Nearest Neighbor

As mentioned in the introduction, only approximately 60

percent correct matches appear in the first nearest neighbor.

Thus, the ability to consider multiple nearest neighbors is

important to a candidate matches filter algorithm. To deter-

mine the difference between using multiple nearest neighbors

and using only the first nearest neighbor, Figure 8 presents a

comparison between using the nearest neighbor distance ratio

(0.8 is used in the test) (Lowe, 2004) and using our algorithm.

The images were captured by the five-view camera TOP-

DC-5. The images on the left side of Figure 8 were captured by

the nadir lens, and the images on the right were captured by

the 45-degree tilt lens. Three corresponding areas are marked

out as A, B, and C in the im-

ages. The image is an urban

area with many tall build-

ings inside; several pixels

are occluded by the build-

ings in the second image.

Figure 8a shows the original

images. Figure 8b shows the

matching result using the

nearest neighbor distance

ratio algorithm. Figure 8c

shows the matching result

using our algorithm. Ad-

ditional correct matches

are found by our algorithm,

although some small error

matches remain. Neverthe-

less, finding additional

correct matches results in

a much higher number of

tie-points, with more than

three observations within

the working block. This

result is important for the

bundle adjustment. Fur-

thermore, those small error

matches can be eliminated

during the bundle adjust-

ment.

Difference between using

LMedS Directly and Using Our

Algorithm as a preprocessing

Step before LMedS

The

LMedS

works effectively

if the outlier is not highly

significant. Thus, to show

the ability of our algorithm,

five test data are collected

to make the comparison.

They are chosen consid-

ering their difficulty in

matching; furthermore, the

inlier of initial matches are

all less than 10 percent.

These five test data are

aerial images with ground

references, that is, the ori-

entation parameters of the

images and the elevation of

the scene are known. Thus,

the correctness of a match

can be known by comparing

the projection coordinate

and the image coordinate

of a match point. In our test, a match is taken as correct if the

Euclidean distance between

and

is less than 2 pixels.

For these five test data, using

LMedS

directly to remove the

outliers all failed. However, if we use our algorithm as a pre-

processing step before using the

LMedS

, then the outliers can

be removed successfully. This is because our algorithm can

remove most outliers while protecting most inliers.

Table 1 shows the performance of our algorithm. The ratio

of correct matches in initial matches is shown in the sixth col-

umn,

of Table 1. For each feature point, eight nearest

neighbors are retained at the beginning; thus, the number of

initial matches in column

is the number of feature points

times 8. The total number of correct matches in the initial

matches is provided in column

. The number of matches

Figure 8. Difference between using multiple nearest neighbors and using only the first nearest neighbor:

(a) The original image pairs, (b) The matching result of using only the first nearest neighbor, and (c) The

matching result of using multiple nearest neighbors.

564

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

SEO Version