Atmospheric correction is the process of removing the scattering and absorption effects of the atmosphere on the reflectance values of images taken by satellite or airborne sensors.[1][2] Atmospheric effects in optical remote sensing are significant and complex, dramatically altering the spectral nature of the radiation reaching the remote sensor.[3] The atmosphere both absorbs and scatters various wavelengths of the visible spectrum which must pass through the atmosphere twice, once from the sun to the object and then again as it travels back up the image sensor. These distortions are corrected using various approaches and techniques, as described below.[4]

Examples of Atmospheric Correction Methods

Examples of atmospheric correction techniques for multispectral remote-sensing images, ordered chronologically to show the historical development of atmospheric correction methods in remote-sensing.
Sensor Approach
MSSband-to-band regression [5]
MSSall-band spectral covariance [6]
airborne MSSband-to-band regression [7]
AVHRRiterative estimation [8]
MSS, TMDOS with exponential scattering model [9]
TMDOS with exponential scattering model, downwelling atmospheric radiance measurements [10]
TMpixel-by-pixel tasseled cap haze parameter [11]
AVHRRDOS, NDVI, AVHRR band 3 [12]
airborne TMS, Landsat TMground and airborne solar measurements, atmospheric modeling code [13]
TMcomparison of ten DOS and atmospheric modeling code variations with field data [14]
TMdark target, modeling code [15]
TM (all bands)atmospheric modeling code, region histogram matching [16]
TMDOS with estimated atmospheric transmittance [17]
TMdark target, atmospheric modeling code
TM, ETM+empirical line method, single target, ground measurements
TMwater reservoirs, comparison of 7 methods for 12 dates
AVHRR2-band PCT used to separate aerosol components

See also

References

  1. Pacifici, F.; Longbotham, N.; Emery, W. J. (2014-10-01). "The Importance of Physical Quantities for the Analysis of Multitemporal and Multiangular Optical Very High Spatial Resolution Images". IEEE Transactions on Geoscience and Remote Sensing. 52 (10): 6241–6256. Bibcode:2014ITGRS..52.6241P. doi:10.1109/TGRS.2013.2295819.
  2. "Atmospheric Correction". University of Maryland Institute for Advanced Computer Studies. Archived from the original on 7 September 2008. Retrieved 2008-08-18.
  3. Schowengerdt, Robert (2007). Remote Sensing: Models and Methods for Image Processing. Elsevier Inc. p. 337. ISBN 978-0-12-369407-2.
  4. Schowengerdt, Robert (2007). Remote Sensing: Models and Methods for Image Processing. Elsevier Inc. p. 338. ISBN 978-0-12-369407-2.
  5. Potter, J. F.; Mendolowitz, M. (1975). On the determination of the haze levels from Landsat data. 10th International Symposium on Remote Sensing of Environment. NASA United States. pp. 695–703. 19760052102.
  6. Switzer, P.; Kowalik, W. S.; Lyon, R. J. (1981). "Estimation of atmospheric path radiance by the covariance matrix method". Photogrammetric Engineering and Remote Sensing. 47: 1469–1476.
  7. Potter, J. F. (1984). "The channel correlation method for estimating aerosol levels from multispectral scanner data". Photogrammetric Engineering and Remote Sensing. 50: 43–52.
  8. Singh, S. M.; Cracknell, A. P. (1986). "The estimation of atmospheric effects for SPOT using AVHRR channel-1 data". International Journal of Remote Sensing. 7 (3): 361–377. Bibcode:1986IJRS....7..361S. doi:10.1080/01431168608954692.
  9. Chavez, P. S. (1988). "An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data". Remote Sensing of Environment. 24 (3): 459–479. Bibcode:1988RSEnv..24..459C. doi:10.1016/0034-4257(88)90019-3.
  10. Chavez, P. S. (1989). "Radiometric calibration of Landsat Thematic Mapper multispectral images". Photogrammetric Engineering and Remote Sensing. 55 (9): 1285–1294.
  11. Lavreau, J. (1991). "De-hazing Landsat Thematic Mapper images". Photogrammetric Engineering and Remote Sensing. 57 (10): 1297–1302.
  12. Holben, B.; Vermote, E.; Kaufman, Y. J.; Tanre, D.; Kalb, V. (1992). "Aerosol retrieval over land from AVHRR data - application for atmospheric correction". IEEE Transactions on Geoscience and Remote Sensing. 30 (2): 212–222. Bibcode:1992ITGRS..30..212H. doi:10.1109/36.134072.
  13. Wrigley, R. C.; Spanner, M. A.; Slye, R. E.; Pueschel, R. F.; Aggarwal, H. R. (1992). "Atmospheric correction of remotely sensed image data by a simplified model". Journal of Geophysical Research. 97 (D17): 18797–18814. Bibcode:1992JGR....9718797W. doi:10.1029/92JD01347.
  14. Moran, M. S.; Jackson, R. D.; Slater, P. N.; Teillet, P. M. (1992). "Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output". Remote Sensing of Environment. 41 (2–3): 169–184. Bibcode:1992RSEnv..41..169M. doi:10.1016/0034-4257(92)90076-V.
  15. Teillet, P. M.; Fedosejevs, G. (1995). "On the dark target approach to atmospheric correction of remotely sensed data". Canadian Journal of Remote Sensing. 21 (4): 374–387. Bibcode:1995CaJRS..21..374T. doi:10.1080/07038992.1995.10855161.
  16. Richter, R. (1996). "A spatially adaptive fast atmospheric correction algorithm". International Journal of Remote Sensing. 17 (6): 1201–1214. Bibcode:1996IJRS...17.1201R. doi:10.1080/01431169608949077.
  17. Chavez, P. S. Jr. (1996). "Image-based atmospheric corrections-revisited and improved". Photogrammetric Engineering and Remote Sensing. 62 (9): 1025–1036.
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