CIMSS Seminar Series:
22 May 2007
Investigating High Spectral Resolution Data with Principle Component Analysis
Dave Tobin
Exploiting the redundancy in high spectral resolution observations, dependent set Principle Component Analysis (PCA) is a simple yet very powerful tool not only for noise filtering and lossy compression, but also for the characterization of sensor noise and other variable artifacts present in Earth scene data. An approach for dependent set PCA of hyperspectral Earth scene data will be presented along with sample results from the Scanning High resolution Interferometer Sounder (S-HIS), the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI).
