Recent efforts to validate EOS observations. Hyperspectral data noise characterization using PCA: application to AIRS

David Tobin; Henry Revercomb; Paolo Antonelli; Kenneth Vinson; Steven Dutcher; Robert Knuteson; Joseph Taylor; Fred Best; Chris Moeller; Mathew Gunshor

doi:10.1117/12.683981

1 September 2006 Recent efforts to validate EOS observations. Hyperspectral data noise characterization using PCA: application to AIRS

David Tobin, Henry Revercomb, Paolo Antonelli, Kenneth Vinson, Steven Dutcher, Robert Knuteson, Joseph Taylor, Fred Best, Chris Moeller, Mathew Gunshor

Author Affiliations +

Proceedings Volume 6301, Atmospheric and Environmental Remote Sensing Data Processing and Utilization II: Perspective on Calibration/Validation Initiatives and Strategies; 630107 (2006) https://doi.org/10.1117/12.683981
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

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 using Earth scene data. Our approach for dependent set PCA of AIRS Earth scene data is presented. Aspects of the analyses include 1) estimation of NEDT using PCA and comparisons to values derived from on-board blackbodies, 2) estimation of the scene dependence of NEDN, 3) estimation of the spectrally correlated component of NEDT and comparison to pre-launch analyses using blackbody views, 4) investigation of non- Gaussian noise behavior, and 5) inspection of individual PCs. The results of the PCA analyses are generally consistent with results obtained pre-launch and on-orbit using blackbody and/or space view data. Specific findings include: 1) PCA estimates of AIRS spectrally random and spectrally correlated NEDN compare well with estimates computed from on-board blackbody and space views, 2) the signal dependence of AIRS NEDN is accurately parameterized in terms of the scene radiance, 3) examination of the reconstruction error allows non-Gaussian phenomenon such as popping to be characterized, and 4) inspection of the PCs and individual PC filtered radiance spectra is a powerful technique for diagnosing low level artifacts in hyperspectral data.

Citation Download Citation

David Tobin, Henry Revercomb, Paolo Antonelli, Kenneth Vinson, Steven Dutcher, Robert Knuteson, Joseph Taylor, Fred Best, Chris Moeller, and Mathew Gunshor "Recent efforts to validate EOS observations. Hyperspectral data noise characterization using PCA: application to AIRS", Proc. SPIE 6301, Atmospheric and Environmental Remote Sensing Data Processing and Utilization II: Perspective on Calibration/Validation Initiatives and Strategies, 630107 (1 September 2006); https://doi.org/10.1117/12.683981

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