[VE
red
del
AC
Ive
nd
ter
ter
ral
nd
wo
lly
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
area. Model FLAASH reduces the effect effectively which the
atmosphere has on the remote sensing image to enhance the
image information with better accuracy than Model QUAC, but
it depends on the input parameters and the calibration accuracy
of instrument, so it applies to the cases that the calibration
accuracy of instrument has reached the requirements and
various atmospheric parameters have been known. Although
the accuracy of Model QUAC is less than Model FLAASH, it
depends less on the input parameters and the accuracy of
instrument calibration with its specific applicability. For
example, under the case that change detection is needed for
multi-view images, fast atmospheric correction can be made
first on the two images. Such simple and efficient input set can
help finish the atmospheric correction task of "from figure to
figure". It is certain that the two models also need further
improvement, and enhancing the inversion accuracy in the area
with complex terrain and under the complex atmospheric
conditions is a valuable research direction.
REFERENCES
Chen Shupeng etc, Study of Information Mechanism of Remote
Sensing . Science Press. 1998.
Zhao Yingshi etc, Applications of Remote Sensing Principles
and Methods. Science Press. 2000.
Cheng Wei, Wang Liming, Tian Jiuging. A Method of
Atmospheric Correction Based on Shadow-pixel for Optical
Satellite Data, Journal of Surveying and Mapping, 37(4). 2008.
M.W. Matthew, S. M. Adler-Golden, A. Berk, G. Felde, G. P.
Anderson, D. Gorodetzky, S. Paswaters, and M. Shippert,
“Atmospheric correction of spectral imagery: Evaluation of the
FLAASH algorithm with AVIRIS data,” in Proc. SPIE Conf.
Algorithms and Technologies for Multispectral, Hyperspectral,
and Ultraspectral Imagery IX, 2003.
Song Xiaoyu, Wang Jihua, Liu Liangyun, Huang Wenjiang,
Zhao Chunjiang. Atmospheric Correction of Hyper-spectral
Image: Evaluation of the FLAASH Algorithm with AVRIS
Data. Remote Sensing Technology and Application.2005: 20 (4)
B.-C.Gao, M. J. Montes, C. O. Davis, and FH. Goetz,
“Atmospheric correction algorithms for hyperspectral remote
sensing data of land and ocean”, Remote Sensing of
Environment, 113, pp.17—24, 2009.
Yang Jiaojun, Chen Yushi, Zhang Ye. Effect on Atmospheric
Correction by Inputting Parameters of Model . Remote Sensing
Application. 2008, June.
Berk, A., GP. Anderson, P.K. Acharya, L.S. Bernstein, L.
Muratov, J. Lee, M. Fox, S.M. Adler-Golden, J.H. Chetwynd,
Jr, M.L. Hoke, R.B. Lockwood, J.A. Gardner, T.W. Cooley,
C.C. Borel, PE. Lewis and E.P. Shettle. *MODTRANS:2006
Update,” In Algorithms and Technologies for Multispectral,
Hyperspectral, and Ultraspectral Imagery XII, Sylvia S. Chen,
Paul E. Lewis, Editors, Proceedings of SPIE Vol. 6233, 2006.
Yang Hang, Zhang Xia, Shuai Tong, Tong Qingxi. 2010.
Comparison between FLAASH Method and Empirical Linear
Method of OMIS- II Image Atmospheric Correction. Bulletin of
Surveying and Mapping. 2010, August.
ITT Visual Information Solutions (ITT VIS), *ENVI User's
Guide, Version 4.8", ITT Visual Information Solutions (ITT
VIS), Boulder (CO), USA.2010.
Wu Bin, Miao Fang, Ye Chengming, Huang Shuhanmao, Bi
Xiaojia.Atmospheric Correction of Hyperspectral Remote
Sensing Image Based on FLAASH. Computing Techniques for
Geophysical and Geochemical Exploration. 2010,July
S.M. Adler-Golden, A. Berk, L.S. Bernstein, S. Richtsmeier,
PK. Acharya, M.W. Matthew, GP. Anderson, C. Allred, L.
Jeong, and J. Chetwynd, “FLAASH, A MODTRAN4
Atmospheric Correction Package for Hyperspectral Data
Retrievals and Simulations,” Proc. 7th Ann. JPL Airborne Earth
Science Workshop, Pasadena, Calif., JPL Publication 97-21, pp.
9-14, 1998.
LIANG Shun-lin, FANG Hong-liang, CHEN Ming-zhen.
Atmospheric Correction of Landsat ETM+ Land Surface
Imagery-Part I: Methods. JEEE Transactions on Geo-science &
Remote Sensing, 39(11):2490-2498, 2001.
Perkins, T. S. Adler-Golden, M. Matthew, A. Berk, G
Anderson, J. Gardner and G. Felde, “Retrieval of Atmospheric
Properties from Hyper and Multispectral Imagery with the
FLAASH Atmospheric Correction Algorithm," In Remote
Sensing of Clouds and the Atmosphere X, Klaus Scháfer;
Adolfo T. Comerón; James R. Slusser; Richard H. Picard;
Michel R. Carleer; Nicolaos Sifakis, Editors, Proceedings of
SPIE Vol. 5979, 2005.
Hao Jianting, Yang Wunian, Li Yuxia, Hao Jianyuan.
Atmospheric Correction of Hyperspectral Remote Sensing
Image Based on FLAASH. Remote Sensing Application, 2008,
January.
Wang Jihua etc. An Algorithm Based on 6S Model Removing
Atmospheric Effects from Satellite Imaginery Pixel pixel by
pixel. Optical Technique. 33(1): 11-15, 2007.
Guo Yunkai, Zhang Qisen. Research on Methods of Computer
Classification of Generalized Angle-based Remote Sensing
Image. China Journal of Highway and Transport. 15 (2), 2005.
Liu Yan, Wang Hong, Zhang Pu, Li Yang. Atmospheric
Correction of Landsat TM Imagery by Using Meteorological
Records. Remote Sensing for Land & Resources. 3 88 (1), 2011.
Luo Cailian, Chen Jie, Le Tongchao. Atmospheric Correction
on Landsat ETM+ Satellite Image Based on FLAASH Model.
Protection Forest Science and Technology. 86 (5), 2008.