1 September 2003 Criteria for satellite image restoration success
Author Affiliations +
Many properties of the atmosphere affect the quality of images propagating through it by blurring and reducing their contrast. The atmospheric path involves several limitations, such as scattering, absorption of light, and turbulence, which degrade the image. Recently developed atmospheric filters, which correct for turbulence blur, aerosol blur, and path radiance simultaneously, are implemented here in the digital restoration of Landsat thematic mapper (TM) imagery. The turbulence modulation transfer function (MTF) is calculated from meteorological data or estimated if no meteorological data were measured. Aerosol MTF is consistent with optical depth. The product of the two yields atmospheric MTF, which is implemented in the atmospheric filter. Restoration improves smallness of size of both resolvable detail and contrast. Restorations are quite apparent even under clear weather conditions. A way to evaluate restoration improvement is presented here by the use of quantitative criteria as well as subjective opinions of human observers in perception experiments. Not all the restoration criteria represent improvement in the same tested image under the same restoration conditions. When one criterion suggests an enhancement, there is a chance that another one might represent a lower value for restoration success.
© (2003) Society of Photo-Optical Instrumentation Engineers (SPIE)
Dan Arbel, Dan Arbel, Shlomo Greenberg, Shlomo Greenberg, Ofer Hadar, Ofer Hadar, Norman S. Kopeika, Norman S. Kopeika, } "Criteria for satellite image restoration success," Optical Engineering 42(9), (1 September 2003). https://doi.org/10.1117/1.1599360 . Submission:


Satellite image restoration filter comparison
Proceedings of SPIE (October 04 1999)
Restoration of satellite images based on atmospheric MTF
Proceedings of SPIE (October 20 1996)
Criteria for satellite image restoration success
Proceedings of SPIE (November 12 2000)
Landsat TM satellite image restoration using Kalman filter
Proceedings of SPIE (November 19 2001)
Satellite image restoration filter comparison
Proceedings of SPIE (November 20 2002)

Back to Top