Mr. John B. Lunzer Profile

John B. Lunzer

at University of Puerto Rico Mayaguez

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Proceedings Article | 18 May 2013 Paper

Fully automatic destriping of Hyperion hyperspectral satellite imagery using local window statistics

John Lunzer, Shawn Hunt

Proceedings Volume 8743, 87431R (2013) https://doi.org/10.1117/12.2018860

KEYWORDS: Reflectivity, Sensors, Satellites, Spectroscopy, Calibration, Hyperspectral imaging, Short wave infrared radiation, Image processing, Satellite imaging, Neodymium

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NASA’s EO-1 satellite, well into it’s second decade of operation, continues to provide multispectral and hyperspectral data to the remote sensing community. The Hyperion pushbroom type hyperspectral spectrometer aboard EO-1 can be a rich and useful source of high temporal resolution hyperspectral data. Unfortunately the Hyperion sensor suffers from several issues including a low signal to noise ratio in many band regions as well as imaging artifacts. One artifact is the presence of vertical striping, which, if uncorrected, limits the value of the Hyperion imagery. The detector array reads in all spectral bands one spatial dimension (cross-track) at a time. The second spatial dimension (in-track) arises from the motion of the satellite. The striping is caused by calibration errors in the detector array that appear as a vertical striping pattern in the in-track direction. Because of the layout of the sensor array each spectral band exhibits it’s own characteristic striping pattern, each of which must be corrected independently. Many current Hyperion destriping algorithms focus on the correction of stripes by analyzing the column means and standard deviations of each band. The more effective algorithms utilize windowing of the column means and interband correlation of these window means. The approach taken in this paper achieves greater accuracy and effectiveness due to not only using local windowing in the across track dimension but also along the in‐track. This allows detection of the striping patterns in radiometrically homogeneous areas, providing improved detection accuracy.

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