In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
3.1 Experimental evaluation 
We tested our algorithm using the simulated images 
emphasizing the following: 1.Temporal changes simulated by 
adding and removing structures and lines, 2. Multi- sensor data 
simulated by different spatial resolution (rotation and scaling). 
The evaluation criterion is the repeatability score. Thus, the test 
sequences comprise images of real textured and structured 
scenes. Figure 2 shows that TPE with reasonable accuracy rate 
of >0.9 maintained for temporal change rate of < 40% of 26 
simulated scenario of spatial variances including feature erasure 
and displacement. For cardinal changes (> 40%) the RMS 
threshold in stage 2 (topological map-matching) fails to identify 
the correct CPs pair. 
Figure 2. Temporal change versus registration accuracy, blue 
points are different simulation of temporal changes, black 
hatched line is trend line, and red cursor is RMS threshold of 
topological map-matching stage 
An artificial scaling and rotation of simulated image evaluates 
matching accuracy for multi-sensor dataset. Table 1 summarizes 
the error in displacement, where TPE error represents Test Point 
error for 10% of all CPs pairs. The "Original" corresponds to 
original spatial resolution (0.1m) and orientation (0°) of 
simulated image, "Siml" corresponds to rotation of 100°, 
"Sim 11" corresponds to rotation of 100° and scaling of X2, 
"Sim2" corresponds to rotation of 290°, "Sim2_2" corresponds 
to rotation of 290° and scaling of X2.5. 
Simulated dataset 
Original 
Siml 
Siml_l 
Sim2 
Sim2_2 
TPE 
0.00001 
0.00032 
0.0056 
0.00038 
0.0063 
Table 1. Error (in m) for simulated multi-sensor dataset when 
original image resolution is 0.1 m and orientation is 0° 
3.2 Case Study 
In this study we operated three sensors emphasizing multi- 
sensor registration in two selected time domains where multi 
temporal changes were occurred. The selected sensors 
documented in table 2. Images of three sensors (table 2) was an 
area of 1.5X1.1 km in mid (33°30' / 34°42') Israel. 
Table 3 summarizes the error in displacement of three images 
(Panchromatic scanner 1, and two Panchromatic scanner 2 
images) to Ikonos image from 2008 where TPE error represents 
Test Point error for 10% of all CPs pairs. 
Simulated dataset 
TPE 
Panchromatic scanner 1 
0.001 
Panchromatic scanner 2 
0.0029 
Panchromatic scanner 2 
0.0035 
Table 3. Error (in m) for simulated multi-sensor dataset when 
original images resolution is 1, 0.25, 0.12 m 
4. CONCLUSION 
We propose an AIRTop algorithm as method for the solution of 
the core problem of multi-sensor and multi-temporal images 
registration, based on combination between SURF (Speeded Up 
Robust Features) method and weight-based topological map 
matching algorithm (tMM). The main focus of our algorithm is 
on scale and image rotation invariant the quality of the scanning 
system. Both simulated experimental and real-world case study 
results shows high accuracy of registration process. 
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Sensor 
Type 
Detector 
Spatial 
Resolution 
Radiometric 
resolution 
Date 
Ikonos 
Spacebome 
Pushbroom 
1 m 
11 bit 
06.2008 
Panchromatic 
scanner 1 
Airborne 
Pushbroom 
0.25 m 
12 bit 
02.2009 
Panchromatic 
scanner 2 
Airborne 
Whiskbroom 
0.12m 
8 bit 
06.2009 
Panchromatic 
scanner 2 
Airborne 
Whiskbroom 
0.12m 
8 bit 
07.2009 
9. Moigne, J. L., W. J. Campbell, and R. F. Cromp, 2002. "An 
automated parallel image registration technique based on the 
correlation of wavelet features". IEEE Transaction on 
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resolution f-18 flurodeoxyglu-cose pet images for evaluation of 
Table 2. Selected sensors for case study