hul 2004 
bands of 
solid line 
and the 
ess level 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
Since the two strips are taken with 20, resp. 40 minutes distance 
the solar position has moved. More important is the fact that the 
azimuth has turned by 180 degrees and that the RGB lines in 
this camera are looking 16 degrees forward. So even for the 
center pixels the viewing geometry has changed. 
This can be clearly seen in the original data. When using the 
method described in sec. 3 a considerable reduction can be 
obtained. 
The corresponding column statistics is shown in Figure 7. The 
statistics has got a gap between minus and plus 16 degrees 
because of the tilt angle of the RGB lines, which sets a 
minimum for the viewing angle (However, the ADS40 is also 
available with the RGB lines placed in Nadir position). 
The modelling is sometimes off the data due to the nature of the 
model. However the relative shape is always maintained. It can 
be seen that the data have quite considerable differences in 
brightness level. The asterisk denotes the final view angle and 
brightness level (here the NS line) for which the correction will 
be performed. If the hot spot function is replaced by the Li 
Sparse Reciprocal MODIS kernel from the AMBRALS model 
the modelling quality is not significantly changed. 
5. CONCLUSIONS 
In this paper we have shown a strategy for correction of 
atmospheric and BRDF effects in ADS40 images. 
The requirements for mapping imagery differ from those in 
remote sensing applications. The huge data amounts require fast 
and robust algorithms which produce seamless image mosaics. 
So empirical methods are the first choice unless the data quality 
requires higher accuracy. 
For the case of the atmospheric correction this results in using 
an improved dark pixel method. The BRDF correction is 
performed using an improved Walthall model. 
It was shown with ADS40 RGB image data that the brightness 
gradient could be removed and image brightness of different 
flight lines could be adjusted to match properly, without 
removing image fluctuations. Remaining seams can be removed 
with conventional feathering. 
This is a step towards an automatic generation of huge seamless 
maps. 
6. REFERENCES 
Beisl, U., 2001. New method for correction of bidirectional 
effects in hyperspectral images. Proc. 8" International 
Symposium in Remote sensing (SPIE), Toulouse, France. 
Beisl, U., 2002. Simultaneous correction of bidirectional effects 
in line scanner images of rural areas. Proc. 9" International 
Symposium in Remote sensing (SPIE), Agia Pelagia, Crete. 
Berk, A., Bernstein, L. S., Anderson, G.P., Acharya, P. K., 
Robertson, D.C., Chetwynd, J. H., and Adler-Golden, S. M., 
1998. MODTRAN cloud and multiple scattering upgrades with 
application to AVIRIS. Remote Sens. Environ., 65, pp. 367- 
375. 
Chavez, P. S., Jr, 1975. Atmospheric, solar, and MTF 
corrections for ERTS digital imagery. Proc. Am. Soc. 
Photogrammetry, Fall Technical Meeting, Phoenix, AZ, p. 69. 
Chavez, P. S., Jr., 1988. An improved dark-object subtraction 
technique for atmospheric scattering correction of multispectral 
data. Remote Sens. Environ., 24, pp. 459-479. 
Chopping, M. J., 2000. Testing a LiSK BRDF model with in 
situ bidirectional reflectance factor measurements over semiarid 
grasslands. Remote Sens. Environ., 74, pp. 287-312. 
Dave, J. V., 1980. Effect of atmospheric conditions on remote 
sensing of a surface non-homogeneity. Photogramm. Eng. 
Remote Sens., 46(9), pp. 1173-1180. 
Gao, B.-C., Heidebrecht, K. B., and Goetz, A. F. H., 1993. 
Derivation of scaled surface reflectances from AVIRIS data. 
Remote Sens. Environ., 44, pp. 145-163. 
Leachtenauer, J. C., Malila, W., Irvine, J., Colburn, L., and 
Salvaggio, N., 1997. Appl. Opt., 36(32), pp. 8322-8328. 
Moran, S., Jackson, R. D., Slater. P- N., and Teillet, P. M., 
1992. Evaluation of simplified procedures for retrieval of land 
surface reflectance factors from satellite sensor output. Remote 
Sens. Environ., 41, pp. 169-184. 
Nilson, T., and Kuusk, A., 1989. A reflectance model for the 
homogeneous plant canopy and its inversion. Remote Sens. 
Environ., 27, pp. 157-167. 
Richter, R., 1996. Atmospheric correction of DAIS 
hyperspectral image data. Computers & Geosciences, 22(7), 
pp.785-793. 
Richter, R., and Schlaepfer, D., 2002. Geo-atmospheric 
processing of airborne imaging spectrometry data. Part 2: 
atmospheric/topographic correction. Int. J. Remote Sensing, 23, 
pp. 2631-2649. 
Roberts, D. A., Yamaguchi, Y., and Lyon, R. J. P., 1986. 
Comparison of various techniques for calibration of AIS data. 
Proc. 2"' Airborne Imaging Spectrometer Data Analysis 
Workshop, JPL Publications, Pasadena, CA, pp. 21-30. 
Schott, J. R., 1997. Remote Sensing: The image chain 
approach. Oxford University Press, New York. 
Vermote, E.F., Tanré, D., Deuzé, J. L., Herman, M., and 
Mocrette, J.-J., 1997. Second Simulation of the Satellite Signal 
in the Solar Spectrum, 6S: An overview. /EEE Transact. 
Geosci. Remote Sens., 35(3), pp. 675-686. 
Walthall, C. L., Norman, J. M., Welles, J. M., Campbell, G., 
and Blad, B. L., 1985. Simple equation to approximate the 
bidirectional reflectance from vegetative canopies and bare soil 
surfaces. Appl. Opt., 24(3), pp. 383-387. 
Wanner, W., Li, X., and Strahler, A. H., 1995. On the 
derivation of kernels for kernel-driven models of bidirectional 
reflectance. J. Geophys. Res., 100(D10), pp. 21077-21089. 
7. ACKNOWLEDGEMENTS 
Many thanks to the ground spectrometry team of Dr. 
Kneubühler ‘rom the Remote Sensing Laboratories in Zurich 
who made accompanying BRDF measurements with the RSL 
Goniometer on the sand sports ground in Hinwil, Switzerland.