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New Algorithm Fuses Quality, Quantity in Satellite Imagery


From left, Kaiyu Guan, Yunan Luo, and Jian Peng at the University of Illinois developed the new algorithm

University of Illinois researchers have developed an algorithm that can fuse high-resolution and high-frequency satellite data into one integrated product.

Credit: L. Brian Stauffer

Satellite imagery has long faced a dilemma in whether to sacrifice high spatial resolution in the interest of generating images more frequently, or vice-versa. University of Illinois researchers have developed an algorithm that solves this by combining high-resolution and high-frequency satellite data into one integrated product, with the capability of generating 30-meter daily continuous images dating back to the year 2000.

The algorithm automatically integrates information from existing data, compensating for missing information that comes from cloud coverage or data gaps.

The new program can create images without any missing pixels, for any site or region, by leveraging time-series information and relations with neighboring pixels.

"The data sources for our algorithms use the most rigorous data from [the U.S. National Aeronautics and Space Administration] or the European Space Agency and produce daily fusion data that is ready for research and practical applications," says Kaiyu Guan of the University of Illinois.

From University of Illinois News Bureau
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