Share Email Print

Proceedings Paper

Development of image processing method to detect noise in geostationary imagery
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The Clouds and the Earth’s Radiant Energy System (CERES) has incorporated imagery from 16 individual geostationary (GEO) satellites across five contiguous domains since March 2000. In order to derive broadband fluxes uniform across satellite platforms it is important to ensure a good quality of the input raw count data. GEO data obtained by older GOES imagers (such as MTSAT-1, Meteosat-5, Meteosat-7, GMS-5, and GOES-9) are known to frequently contain various types of noise caused by transmission errors, sync errors, stray light contamination, and others. This work presents an image processing methodology designed to detect most kinds of noise and corrupt data in all bands of raw imagery from modern and historic GEO satellites. The algorithm is based on a set of different approaches to detect abnormal image patterns, including inter-line and inter-pixel differences within a scanline, correlation between scanlines, analysis of spatial variance, and also a 2D Fourier analysis of the image spatial frequencies. In spite of computational complexity, the described method is highly optimized for performance to facilitate volume processing of multi-year data and runs in fully automated mode. Reliability of this noise detection technique has been assessed by human supervision for each GEO dataset obtained during selected time periods in 2005 and 2006. This assessment has demonstrated the overall detection accuracy of over 99.5% and the false alarm rate of under 0.3%. The described noise detection routine is currently used in volume processing of historical GEO imagery for subsequent production of global gridded data products and for cross-platform calibration.

Paper Details

Date Published: 18 October 2016
PDF: 9 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100041S (18 October 2016); doi: 10.1117/12.2241544
Show Author Affiliations
Konstantin V. Khlopenkov, Science Systems and Applications, Inc. (United States)
David R. Doelling, NASA Langley Research Ctr. (United States)

Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?