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Proceedings Paper

Criteria for satellite image restoration success
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Paper Abstract

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 and absorption of the light and turbulence, which degrade the image. The recently developed atmospheric Wiener filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously, is implemented here in digital restoration of Landsat Thematic Mapper (TM) imagery over seven wavelength bands of the satellite instrumentation. Turbulence MTF (Modulation Transfer Function) is calculated from meteorological data or estimated in 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 Wiener filter. Restoration improves both smallness of size of resolvable detail and contrast. Restorations are quite apparent even under clear weather conditions. Different restoration results are obtained by trying to restore the degraded image. A way to determine which is the best restoration result and how good is the restored image is presented here, by examining mathematical criteria such as MSE (Mean Square Error), ROH (Richness of Histogram), and SOH (Similarity of Histogram), to obtain an improved image and consequently better visual restoration results.

Paper Details

Date Published: 13 November 2000
PDF: 12 pages
Proc. SPIE 4116, Advanced Signal Processing Algorithms, Architectures, and Implementations X, (13 November 2000); doi: 10.1117/12.406520
Show Author Affiliations
Dan Arbel, Ben-Gurion Univ. of the Negev (Israel)
Norman S. Kopeika, Ben-Gurion Univ. of the Negev (Israel)


Published in SPIE Proceedings Vol. 4116:
Advanced Signal Processing Algorithms, Architectures, and Implementations X
Franklin T. Luk, Editor(s)

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