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Reducing Earth Topography Resolution for SMAP Mission Ground Tracks Using K-Means ClusteringThe K-means clustering algorithm is used to reduce Earth topography resolution for the SMAP mission ground tracks. As SMAP propagates in orbit, knowledge of the radar antenna footprints on Earth is required for the antenna misalignment calibration. Each antenna footprint contains a latitude and longitude location pair on the Earth surface. There are 400 pairs in one data set for the calibration model. It is computationally expensive to calculate corresponding Earth elevation for these data pairs. Thus, the antenna footprint resolution is reduced. Similar topographical data pairs are grouped together with the K-means clustering algorithm. The resolution is reduced to the mean of each topographical cluster called the cluster centroid. The corresponding Earth elevation for each cluster centroid is assigned to the entire group. Results show that 400 data points are reduced to 60 while still maintaining algorithm performance and computational efficiency. In this work, sensitivity analysis is also performed to show a trade-off between algorithm performance versus computational efficiency as the number of cluster centroids and algorithm iterations are increased.
Document ID
20150007829
Document Type
Conference Paper
External Source(s)
Authors
Rizvi, Farheen
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
May 8, 2015
Publication Date
August 19, 2013
Subject Category
Earth Resources And Remote Sensing
Meeting Information
AIAA Infotech@Aerospace 2013 Conference(Boston, MA)
Distribution Limits
Public
Copyright
Other
Keywords
ground tracks
topography
antenna misalignment
K-means clustering
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