NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit PhysicsCloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.
Document ID
20100017248
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Bankert, Richard L.
(Naval Research Lab. Monterey, CA United States)
Mitrescu, Cristian
(Naval Research Lab. Monterey, CA United States)
Miller, Steven D.
(Colorado State Univ. Fort Collins, CO, United States)
Wade, Robert H.
(Science Applications International Corp. Monterey, CA, United States)
Date Acquired
August 24, 2013
Publication Date
July 1, 2009
Publication Information
Publication: Journal of Applied Meteorology and Climatology
Publisher: American Meteorological Society
Volume: 48
Issue: 7
Subject Category
Meteorology And Climatology
Report/Patent Number
AD-A513424
Funding Number(s)
CONTRACT_GRANT: NNS06AA22G
CONTRACT_GRANT: NNA07CN14A
Distribution Limits
Public
Copyright
Other
Keywords
EXPLICIT PHYSICS ALGORITHMS
IMPLICIT PHYSICS ALGORITHMS
GOES DATA
GOES (GEOSTATIONARY OPERATIONAL ENVIRONMENTAL SATELLITE)
CLASSIFICATION ALGORITHMS
COMPARATIVE ANALYSIS

Available Downloads

There are no available downloads for this record.
No Preview Available