P1.25
Cloud Cover Detection Algorithms for EO-1 Hyperion Imagery (Formerly Poster P5.25)
Michael K. Griffin, MIT Lincoln Laboratory, Lexington, MA; and H. H. K. Burke, D. Mandl, and J. Miller
Clouds can typically be characterized in the VNIR/SWIR by higher reflectance than the underlying surface. Simple techniques which exploit this trait can be used to discriminate clouds from other features with considerable skill. The EO-1 polar-orbiting platform carries three primary sensors which make measurements of the earth's radiance in this wavelength region. Hyperion is an EO-1 hyperspectral sensor with over 200 contiguous narrow spectral bands from 0.4 to 2.5 µm and approximately 30 m spatial resolution with a swath width of 7.5 km.
Many applications using hyperspectral data require cloud-free conditions; a simple cloud cover detection algorithm can be a useful tool to these efforts. Also, the application of a cloud cover detection algorithm on-board the spacecraft, prior to downlink of collected scenes, can provide considerable savings in downlink resources and processing time.
This presentation begins with a simple cloud cover algorithm which utilizes VNIR (0.4 - 1.0 µm) channels only and expands to include more complete cloud characterization algorithms which also use both NIR and SWIR (1.0 - 2.5 µm) channels to discriminate cloud types and cloud/surface features. The algorithms are applied to a variety of Hyperion scenes which depict various cloud types, surface features and seasonal conditions. An attempt is made to contrast these algorithms in terms of computational complexity versus performance.
Poster Session 1, Climatology and Clouds
Monday, 10 February 2003, 10:15 AM-12:00 PM
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