Landsat TM satellite image restoration using Kalman filter

Dan Arbel; Norman S. Kopeika

doi:10.1117/12.448663

20 November 2001 Landsat TM satellite image restoration using Kalman filter

Dan Arbel, Norman S. Kopeika

Proceedings Volume 4474, Advanced Signal Processing Algorithms, Architectures, and Implementations XI; (2001) https://doi.org/10.1117/12.448663
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

Satellites orbit the Earth and obtain continuous imagery of the ground below along their orbital path. The quality of satellite images propagating through the atmosphere is affected by phenomena such as scattering and absorption of light, and turbulence, which degrade the image by blurring it and reducing its contrast. The atmospheric Wiener filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously, is implemented in digital restoration of Landsat TM (Thematic Mapper) imagery. Digital restoration results of Landsat TM imagery using the atmospheric Wiener filter were presented in the past. Here, a new approach for digital restoration of Landsat TM is presented by implementing a Kalman filter as an atmospheric filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously. Turbulence 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 both the atmospheric Wiener and Kalman filters. Restoration improves both smallness of size of resolvable detail and contrast. Restorations are quite apparent even under clear weather conditions. Here, restorations results of the atmospheric Kalman filter are presented along with those for the atmospheric Wiener filter. A way to determine which is the best restoration result and how good is the restored image is presented by a visual comparison and by considering several mathematical criteria. In general the Kalman restoration is superior, and inclusion of turbulence blur also leads to slightly improved restoration.

Citation Download Citation

Dan Arbel and Norman S. Kopeika "Landsat TM satellite image restoration using Kalman filter", Proc. SPIE 4474, Advanced Signal Processing Algorithms, Architectures, and Implementations XI, (20 November 2001); https://doi.org/10.1117/12.448663

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