Improved atmospheric retrievals of hyperspectral data using geometric constraints

Kenneth Ewald; Emmett J. Ientilucci; Alan Buswell; John Jacobson

doi:10.1117/12.2188376

1 September 2015 Improved atmospheric retrievals of hyperspectral data using geometric constraints

Kenneth Ewald, Emmett J. Ientilucci, Alan Buswell, John Jacobson

Proceedings Volume 9611, Imaging Spectrometry XX; 961113 (2015) https://doi.org/10.1117/12.2188376
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

Within urban environments and other complex illumination conditions, tilted target surface or partial sky occlusions from nearby raised objects can have a significant impact on a target’s radiance observed from a spectral sensor. At the pixel level, these terms can be predicted and corrected for by modeling the impact collocated height data has on the scene radiometry. After properly accounting for these impacts, a Lambertian material’s retrieved spectral reflectivity should be the same at any location or orientation within the scene. This paper proposes a novel approach for using this constraint to iterate on atmospheric aerosol parameters until the difference of retrieved spectral reflectance of two pixels of the same material, but under different illumination conditions, is minimized.

Citation Download Citation

Kenneth Ewald, Emmett J. Ientilucci, Alan Buswell, and John Jacobson "Improved atmospheric retrievals of hyperspectral data using geometric constraints", Proc. SPIE 9611, Imaging Spectrometry XX, 961113 (1 September 2015); https://doi.org/10.1117/12.2188376

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available