3-2-11 
are not has the same accuracy in different position of the same scanning line. 
• The conic scanner has high positioning accuracy than whiskbroom scanner when other 
components (GPS, INS, Laser Range Finder and encoder etc.) of the system have the same 
performance. In addition, the conic scanner can increase the LRF’s laser pulses efficiency. 
• For whiskbroom scanner, the positioning accuracy of the system generally decreases with the 
swath angle increases from nadir. The mean square error in scanning direction is larger than 
in other directions (i.e. flight direction and Z-axis). This is caused by the error of encoder that 
used to measure the angle between laser pulse and nadir. The measurement accuracy of angle 
between laser pulse and nadir by encoder has tremendous influence to the positioning 
accuracy of the system with the whiskbroom scanner. The mean square error in flight direction 
has nearly no change when the encoder accuracy decreases. In scanning direction, the mean 
square error is the largest and increases most rapidly with the accuracy decreases of encoder. 
The mean square error in Z-axis increases more rapidly than in other directions with the 
encoder’s accuracy decreases. 
• For conic scanner, the positioning accuracy is changed with the angle of scanner, too. The 
mean square error in Z-axis is larger than in other directions (i.e. flight direction and Y-axis). 
• ALIMS use differential GPS (DGPS) to determine the position of the Laser-Ranging & 
Mutispectral-Imaging Coupled Scanner (LRMICS). One of the GPS receivers is mounted on 
aircraft and one or more are located at base station(s) that coordinates are known. The 
positioning accuracy of GPS has nearly the same influence in scanning direction, flight 
direction and Z-axis for whiskbroom and conic scanner. 
• INS can provides attitude information of ALIMS. The positioning accuracy of ALIMS decreases 
with the accuracy decreases of INS. For whiskbroom and conic scanner, the INS measurement 
accuracy has nearly the equal influence to positioning accuracy in X, Y and Z-axis. 
• The positioning accuracy of the ALIMS decreases with the increases of flight height. For 
whiskbroom scanner, the mean square error in flight direction is smaller than in scanning 
direction and Z-axis. The mean square error increases in Z-axis is not tremendous with the 
flight height increase. For conic scanner, the increase of flight height nearly doesn’t increase 
the mean square error in Z-axis. But the mean square error in X-axis increases rapidly. 
• The ranging accuracy depends among others on the signal-to-noise ratio (SNR) of the LRF. 
SNR depends on (1) pulse intensity, (2) atmospheric transmittance (largely determined by 
moisture), (3) size of the footprint, (4) reflection characteristics of the terrain, (5) size of the 
detector aperture, and (6) sensitivity of the detector (Lemmens, 1997). For whiskbroom 
scanner, when the measurement accuracy of LRF decreases and the measurement accuracy of 
other sensors are not changed, the mean square errors in flight direction and scanning 
direction have nearly not increase. The mean square error in Z-axis increases rapidly with 
measurement accuracy decreases of LRF. For conic scanner, when the measurement accuracy 
of LRF decreases and the measurement accuracy of other sensors are not changed, the mean 
square error in flight direction has nearly not change and increase slightly in Y-axis. The mean 
square error in Z-axis increases rapidly with measurement accuracy decreases of LRF. When 
LRF has high measurement accuracy, the mean square error in flight direction is larger than 
in other directions for both whiskbroom and conic scanner. 
• The positioning accuracy of georeferenced imagery is related to the interpolation techniques 
that are used for generating DEM form the coordinates of LFR’s footprints. 
References 
1. Estep, L., Lillycrop, W.J., and Parson L. 1994. Sensor Fusion for Hydrographic Mapping Application, U.S. 
Army Corps of Engineers 1994 Training Symposium, Surveying & Mapping Remote Sensing/GIS, Session 
SM-2B, pp. 1-7, New Orleans, LA. 
2. Flood, M. and Gutelius, B. 1997. Commercial Implications of Topographic Terrain Mapping Using 
Scanning Airborne Laser Radar, PE&RS, Vol.53, No.4, pp.327-366. 
3. Haala, N., Gramer, M. and Killian J., 1996. Sensor Fusion for Airborne 3D Data Capture, The Second Int. 
Airborne Remote Sensing Conference and Exhibition, Vol. I, pp.344-353, San Francisco, California. 
4. Hug, C. 1996. Urban Topography Survey with Scanning Laser Altitude and Reflectance Sensor (SCALARS),