By Topic

Atmospheric correction of hyperspectral data over dark surfaces via simulated annealing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Marion, R. ; Lab. of Remote Sensing, CEA, Bruyeres-le-Chatel, France ; Michel, R. ; Faye, C.

A method [atmospheric correction via simulated annealing (ACSA)] is proposed that enhances the atmospheric correction of hyperspectral images over dark surfaces. It is based on the minimization of a smoothness criterion to avoid the assumption of linear variations of the reflectance within gas absorption bands. We first show that this commonly used approach generally fails over dark surfaces when the signal to noise ratio strongly declines. In this case, important residual features highly correlated with the shape of gas absorption bands are observed in the estimated surface reflectance. We add a geometrical constraint to deal with this correlation. A simulated annealing approach is used to solve this constrained optimization problem. The parameters involved in the implementation of the algorithm (initial temperature, number of iterations, cooling schedule, and correlation threshold) are automatically determined by using a standard simulated annealing theory, reflectance databases, and sensor characteristics. Applied to a HyMap image with available ground truths, we verify that ACSA adequately recovers ground reflectance over clear land surfaces, and that the added spectral shape constraint does not introduce any spurious feature in the spectrum. The analysis of an AVIRIS image of Central Switzerland clearly shows the ability of the method to perform enhanced water vapor estimations over dark surfaces. Over a lake (reflectance equal to 0.02, low signal to noise ratio equal to about 6), ACSA retrieves unbiased water vapor amounts (2.86 cm±0.36 cm) in agreement with in situ measurements (2.97 cm±0.30 cm). This corresponds to a reduction of the standard deviation by a factor 3 in comparison with standard unconstrained procedures (1.95 cm±1.08 cm). Similar results are obtained using a Hyperion image of DoE ARM SGP test site containing a very dark area of the land surface.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:44 ,  Issue: 6 )