Full text: Mapping without the sun

185 
MS Data 
PAN data 
Point 
Column 
Row 
Column 
Row 
Upper Left 
2865 
5047 
11200 
20000 
Lower Right 
5937 
7007 
24000 
29000 
Table 1. Data Clipping Information 
Because the Quickbird level 1A (Basic) panchromatic and 
multi-spectral images are not registered, so we firstly use 11 tie 
points to register these two images together. Table 1 shows the 
image coordinates both on the MS and PAN images of the 11 
tie points. Table 2 shows the relative deviation after sensor 
model refinement based on 11 tie points. 
Fig. 6. Test AreaofQmckBiidPancromatic Image. 
No 
Column 
Row 
Column 
Row 
(MS) 
(MS) 
(PAN) 
(PAN) 
1 
333 
612 
1616 
1162 
2 
241 
1711 
1245 
5561 
3 
93 
506 
655 
738 
4 
99 
509 
681 
750 
5 
91 
497 
648 
702 
6 
96 
500 
669 
714 
7 
101 
503 
689 
726 
8 
94 
493 
659 
687 
9 
95 
490 
665 
672 
10 
101 
493 
687 
685 
11 
106 
496 
707 
696 
Table 2: Image coordinates of tie points 
No 
X (m) 
Y(m) 
H (m) 
Relative 
Deviation 
(m) 
1 
694449.8569 
5079301.627 
24.08808 
1.964959 
2 
694301.2212 
5076470.197 
10.54723 
1.036042 
3 
693836.4916 
5079540.694 
12.233693 
1.050535 
4 
693851.8522 
5079533.812 
12.487128 
0.83709 
5 
693830.7191 
5079563.427 
11.754251 
0.240147 
6 
693843.5594 
5079556.412 
11.97922 
0.810582 
7 
693856.3999 
5079549.398 
12.207639 
0.851639 
8 
693837.9503 
5079574.036 
11.600745 
1.333417 
9 
693840.2229 
5079581.826 
11.448312 
1.352583 
10 
693855.5817 
5079574.94 
11.681816 
1.071744 
11 
693868.4209 
5079567.923 
11.898181 
1.439133 
Mean relative deviation= 1.089807m 
Table 3: The relative position deviation after sensor model 
refinement. 
No 
MS image 
PAN image 
Correlation 
score 
Speed 
Column 
Row 
Column 
Row 
1 
1482 
428 
6204.137 
430.4082 
2.842833 
29.08994 
2 
76 
472 
590.4004 
604.8008 
2.966443 
27.18536 
3 
151.5 
481.5 
892.0713 
642.6426 
2.942547 
27.99238 
4 
204.5 
523 
1103.818 
808.9082 
2.903338 
31.03811 
5 
256 
567.5 
1310.333 
988.334 
2.966741 
51.93726 
6 
2721.5 
577.5 
11149.57 
1043.715 
2.693181 
161.1418 
7 
1865 
632 
7733.727 
1248.818 
2.882046 
47.39936 
S 
2690 
679 
11030.43 
1424.285 
2.875981 
124.2371 
9 
329.75 
705 
1598.667 
1530.666 
2.758976 
33.64785 
10 
1651.667 
710.3335 
6882.461 
1560.77 
2.697205 
45.02204 
11 
1666.5 
711.5 
6940.916 
1569.5 
2.776286 
71.46515 
12 
1651.5 
722 
6883.5 
1607.5 
2.692057 
69.60785 
13 
186.5 
723 
1036.25 
1611.125 
2.899198 
102.6754 
14 
1901.5 
723 
7879.75 
1611.625 
2.815388 
41.65556 
15 
1659.5 
727 
6912.5 
1626.5 
2.799026 
22.3505 
16 
1888.4 
727.7998 
7827.834 
1631.166 
2.852297 
49.07752 
17 
1890.556 
728.8887 
7837.285 
1636 
2.881206 
29.50319 
18 
1661.5 
730.5 
6921.285 
1643.572 
2.698721 
55.90456 
19 
1666.5 
732 
6943 
1648.5 
2.700071 
69.18536 
20 
1659.5 
734 
6913.5 
1656.334 
2.827407 
47.928 
21 
1647.429 
746.2856 
6866.75 
1702 
2.790857 
54.14523 
22 
1731.5 
748 
7198.5 
1711.666 
2.864279 
17.32987 
23 
1660 
749 
6913.5 
1715.5 
2.893109 
19.77488 
24 
1691 
823 
7038 
2011.199 
2.982813 
23.2686 
25 
1612.5 
830.5 
6726.4 
2040.801 
2.667052 
45.46385 
26 
544.2 
873.2002 
2459.715 
2211 
2.77232 
31.57893 
27 
402.8333 
885 
1898 
2257.824 
2.912515 
65.67041 
28 
372 
889.5 
1775.5 
2274.5 
2.888473 
69.76954 
29 
606.75 
1229 
2706.857 
3625.285 
2.685123 
40.73128 
30 
2178 
1299.75 
8990.5 
3917 
2.822492 
139.4157 
31 
669.6001 
1320.8 
2954.429 
3992.143 
2.705616 
67.707 
32 
1937.375 
1382.375 
8033.879 
4246.391 
2.870057 
205.0521 
33 
1943 
1395 
8036.666 
4301.666 
2.774447 
81.18978 
34 
1011.125 
1556.875 
4313.75 
4942.25 
2.720715 
105.4436 
35 
1131 
1572.5 
4814.125 
5010.25 
2.619476 
204.4896 
Table 4: Vehicle information extracted. 
From the table 4 we can find that some vehicles are moving 
very slow and some are very fast. We checked some of them 
and found some error caused by following reasons. 
(1) Two cars together 
Some cars are very close to each other. They can not be 
differentiated in the MS image. But they can be differentiated in 
the PAN image. This makes the difference of the vehicle’s 
image position on the PAN image and the MS image. 
Two cars 
Figure 8. Two cars together 
(2) Different reflectance 
Some cars have obvious different reflectance in the PAN image 
and the MS image, especially the dark car. This results in some 
error in vehicle’s image position.
	        
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