Health & Environmental Research Online (HERO)


Print Feedback Export to File
2648285 
Journal Article 
Estimation of Normalized Atmospheric Point Spread Function and Restoration of Remotely Sensed Images 
Semenov, AA; Moshkov, AV; Pozhidayev, VN; Barducci, A; Marcoionni, P; Pippi, I 
2011 
Yes 
IEEE Transactions on Geoscience and Remote Sensing
ISSN: 0196-2892
EISSN: 1558-0644 
49 
7 
2623-2634 
WOS:000292111800013 
The Earth's atmosphere heavily affects the remote sensing images collected by spaceborne passive optical sensors due to radiation-matter interaction phenomena like radiation absorption, scattering, and thermal emission. A complex phenomenon is the adjacency effect, i.e., radiation reflected by the ground that, due to the atmospheric scattering, is being seen in a viewing direction different from that corresponding to the ground location that reflected it. Adjacency gives rise to crosstalk between neighboring picture elements up to a distance that depends on the width of the integral kernel function employed for the mathematical modeling of the problem. As long as the atmosphere is a linear space-invariant system, the adjacency can be modeled as a low-pass filter, with the atmospheric point spread function (APSF) applied to the initial image. In this paper, a direct method of estimating the discrete normalized APSF (NAPSF) using images gathered by high-resolution optical sensors is discussed. We discuss the use of the NAPSF estimate for deducing the Correction Spatial high-pass Filter (CSF)-a correction filter that removes the adjacency effect. The NAPSF estimation procedure has been investigated using statistical simulations, whose outcomes permitted us to identify the conditions under which the NAPSF could be measured with acceptable errors. The NAPSF estimation is examined for various natural images acquired by MOMS-2P, CHRIS, AVIRIS, and MIVIS. 
Adjacency effect; atmospheric correction; atmospheric optics; image restoration; normalized atmospheric point spread function (NAPSF); point spread functions; remote sensing; spatial filtering; stray light