Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 53, Issue 5, Pages 2275-2285Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2014.2352319
Keywords
Cloud layer separation; image decomposition; multispectral image analysis; passive atmospheric tomography; scale separation; sparse optimization; total variation minimization
Categories
Funding
- National Aeronautics and Space Administration (NASA) Earth Science Technology Office [AIST-QRS-12-0003]
- NASA's Radiation Sciences programs
- National Science Foundation [DMS 1217239]
- Direct For Mathematical & Physical Scien [1217239] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [1217239] Funding Source: National Science Foundation
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We introduce a methodology for separating reflective layers of clouds in Earth remote sensing images. We propose a single-channel layer separation framework and extend it to multispectral layer separation. Efficient alternating minimization and fast operator-splitting methods are used to solve minimization problems. Specifically, we apply our methodology to separate strongly stratified and optically thin upper (cirrus) clouds from optically thick lower convective (cumulus) clouds in atmospheric imagery approximated as additive contributions to the observed signal. After setting up synthetic truth scenarios, we evaluate the accuracy of the two-layer separation results while varying the effective opaqueness of each of two types of cloud. We show that multispectral cloud layer separation is consistently more accurate than channel-by-channel cloud layer separation.
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