Relative Radiometric Correction of Cloudy Multitemporal Satellite Imagery
Repeated observation of a given area over time yields
potential for many forms of change detection analysis. These
repeated observations are confounded in terms of radiometric
consistency due to changes in sensor calibration over time,
differences in illumination, observation angles and variation in
atmospheric effects.
This paper demonstrates applicability of an empirical relative
radiometric normalization method to a set of multitemporal cloudy
images acquired by Resourcesat1 LISS III sensor. Objective of this
study is to detect and remove cloud cover and normalize an image
radiometrically. Cloud detection is achieved by using Average
Brightness Threshold (ABT) algorithm. The detected cloud is
removed and replaced with data from another images of the same
area. After cloud removal, the proposed normalization method is
applied to reduce the radiometric influence caused by non surface
factors. This process identifies landscape elements whose reflectance
values are nearly constant over time, i.e. the subset of non-changing
pixels are identified using frequency based correlation technique. The
quality of radiometric normalization is statistically assessed by R2
value and mean square error (MSE) between each pair of analogous
band.
[1] Yang, X.J., and C.P. Lo. "Relative radiometric normalization
performance for change detection from multi-date satellite images."
Photogrammetric Engineering and Remote Sensing, Vol. 66, No. 8, pp.
967-980, 2000.
[2] Fraser, R.S., Ferrare, R.A., Kaufman, Y.J., Markham, B.L. and Mattoo,
S., 1992, "Algorithm for Atmospheric Corrections of Aircraft and
Satellite Imagery", International Journal of Remote Sensing, vol.
13(3), pp. 541-557.
[3] Smith, M.O., Ustin, S.L., Adams, J.B. and Gillespie, A.R., 1990,
"Vegetation in Deserts: I. A Regional Measure of Abundance from
Multispectral Images", Remote Sensing of Environment, vol. 31, pp. 1-
26.
[4] Teillet, P.M., & Fedosejevs, G., 1995, "On the Dark Target Approach
to Atmospheric Correction of Remotely Sensed Data", Canadian
Journal of Remote Sensing, vol. 21(4), pp. 374-387.
[5] Chavez, P.S. Jr., 1988, "An Improved Dark-object Subtraction
Technique for Atmospheric Scattering Correction of Multispectral
Data", Remote Sensing of Environment, vol. 24, pp. 459-479.
[6] Chavez, P.S. Jr. and MacKinnon, D.J., 1994, "Automatic Detection of
Vegetation Changes in the Suthwestern United States using Remotely
Sensed Images", Photogrammetric Engineering and Remote sensing,
vol. 60, pp. 571-583.
[7] Hall, F.G., Strebel, D.E., Nickeson, J.E. and Goetz, S.J., 1991,
"Radiometric Rectification: Toward a Common Radiometric Response
among Multidate, Multisensor Images", Remote Sensing of
Environment, vol. 35, pp. 11-27.
[8] Schott, J.R., Salvaggio, C. and Volchok, W., 1988, "Radiometric Scene
Normalisation using Pseudo Invariant Features", Remote Sensing of
Environment, vol. 26, pp. 1-6.
[9] J Yuan, D. and Elvidge, C.D., 1996, "Comparison of Relative
Radiometric Normalization Techniques", ISPRS Journal of
Photogrammetry and Remote Sensing, vol. 51, pp. 117-126.
[10] F. Cheevasuvit, K. Dejhan and T. Tanapapanich, "Cloud Cover and
Cloud Shadow Removing Based on 2-Dimensional Histogram", ACRS
1992, Oct 1992.
[11] Jensen, J.R. (1983). "Urban/suburban land use analysis." In. R. N.
Colwell (Ed.), Manual of Remote Sensing, 2nd ed., American Society
of Photogrammetry, Falls Church, VA, pp. 1571-1666.
[12] Elvidge, C.D., D. Yuan, R.D. Werackoon, and R.S. Lunetta (1995).
"Relative Radiometric Normalization of Landsat Multispectral Scanner
(MSS) Data using an Automated Scattergram Controlled Regression."
Photogrammetric Engineering and Remote Sensing, Vol. 61, No. 10,
pp. 1255-1260.
[13] Gang Hong, Yun Zhang, "Radiometric Normalization Of Ikonos Image
Using Quickbird Image For Urban Area Change Detection",
Department of Geodesy and Geometrics Engineering, University of
New Brunswick, 2002.
[14] Isaac J.H.Leung, JamesE.Jordan, "Image Processing for Weather
Satellite Cloud Segmentation",IEEE Transactions Geoscience and
Remote Sensing ,Vol.51,No.5,pp. 953-95,1995.
[1] Yang, X.J., and C.P. Lo. "Relative radiometric normalization
performance for change detection from multi-date satellite images."
Photogrammetric Engineering and Remote Sensing, Vol. 66, No. 8, pp.
967-980, 2000.
[2] Fraser, R.S., Ferrare, R.A., Kaufman, Y.J., Markham, B.L. and Mattoo,
S., 1992, "Algorithm for Atmospheric Corrections of Aircraft and
Satellite Imagery", International Journal of Remote Sensing, vol.
13(3), pp. 541-557.
[3] Smith, M.O., Ustin, S.L., Adams, J.B. and Gillespie, A.R., 1990,
"Vegetation in Deserts: I. A Regional Measure of Abundance from
Multispectral Images", Remote Sensing of Environment, vol. 31, pp. 1-
26.
[4] Teillet, P.M., & Fedosejevs, G., 1995, "On the Dark Target Approach
to Atmospheric Correction of Remotely Sensed Data", Canadian
Journal of Remote Sensing, vol. 21(4), pp. 374-387.
[5] Chavez, P.S. Jr., 1988, "An Improved Dark-object Subtraction
Technique for Atmospheric Scattering Correction of Multispectral
Data", Remote Sensing of Environment, vol. 24, pp. 459-479.
[6] Chavez, P.S. Jr. and MacKinnon, D.J., 1994, "Automatic Detection of
Vegetation Changes in the Suthwestern United States using Remotely
Sensed Images", Photogrammetric Engineering and Remote sensing,
vol. 60, pp. 571-583.
[7] Hall, F.G., Strebel, D.E., Nickeson, J.E. and Goetz, S.J., 1991,
"Radiometric Rectification: Toward a Common Radiometric Response
among Multidate, Multisensor Images", Remote Sensing of
Environment, vol. 35, pp. 11-27.
[8] Schott, J.R., Salvaggio, C. and Volchok, W., 1988, "Radiometric Scene
Normalisation using Pseudo Invariant Features", Remote Sensing of
Environment, vol. 26, pp. 1-6.
[9] J Yuan, D. and Elvidge, C.D., 1996, "Comparison of Relative
Radiometric Normalization Techniques", ISPRS Journal of
Photogrammetry and Remote Sensing, vol. 51, pp. 117-126.
[10] F. Cheevasuvit, K. Dejhan and T. Tanapapanich, "Cloud Cover and
Cloud Shadow Removing Based on 2-Dimensional Histogram", ACRS
1992, Oct 1992.
[11] Jensen, J.R. (1983). "Urban/suburban land use analysis." In. R. N.
Colwell (Ed.), Manual of Remote Sensing, 2nd ed., American Society
of Photogrammetry, Falls Church, VA, pp. 1571-1666.
[12] Elvidge, C.D., D. Yuan, R.D. Werackoon, and R.S. Lunetta (1995).
"Relative Radiometric Normalization of Landsat Multispectral Scanner
(MSS) Data using an Automated Scattergram Controlled Regression."
Photogrammetric Engineering and Remote Sensing, Vol. 61, No. 10,
pp. 1255-1260.
[13] Gang Hong, Yun Zhang, "Radiometric Normalization Of Ikonos Image
Using Quickbird Image For Urban Area Change Detection",
Department of Geodesy and Geometrics Engineering, University of
New Brunswick, 2002.
[14] Isaac J.H.Leung, JamesE.Jordan, "Image Processing for Weather
Satellite Cloud Segmentation",IEEE Transactions Geoscience and
Remote Sensing ,Vol.51,No.5,pp. 953-95,1995.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59564", author = "Seema Biday and Udhav Bhosle", title = "Relative Radiometric Correction of Cloudy Multitemporal Satellite Imagery", abstract = "Repeated observation of a given area over time yields
potential for many forms of change detection analysis. These
repeated observations are confounded in terms of radiometric
consistency due to changes in sensor calibration over time,
differences in illumination, observation angles and variation in
atmospheric effects.
This paper demonstrates applicability of an empirical relative
radiometric normalization method to a set of multitemporal cloudy
images acquired by Resourcesat1 LISS III sensor. Objective of this
study is to detect and remove cloud cover and normalize an image
radiometrically. Cloud detection is achieved by using Average
Brightness Threshold (ABT) algorithm. The detected cloud is
removed and replaced with data from another images of the same
area. After cloud removal, the proposed normalization method is
applied to reduce the radiometric influence caused by non surface
factors. This process identifies landscape elements whose reflectance
values are nearly constant over time, i.e. the subset of non-changing
pixels are identified using frequency based correlation technique. The
quality of radiometric normalization is statistically assessed by R2
value and mean square error (MSE) between each pair of analogous
band.", keywords = "Correlation, Frequency domain, Multitemporal,
Relative Radiometric Correction", volume = "3", number = "3", pages = "519-5", }