By Topic

Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions

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

2 Author(s)
Healey, G. ; Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA ; Slater, D.

The spectral radiance measured by an airborne imaging spectrometer for a material on the Earth's surface depends strongly on the illumination incident of the material and the atmospheric conditions. This dependence has limited the success of material-identification algorithms that rely on hyperspectral image data without associated ground-truth information. In this paper, the authors use a comprehensive physical model to show that the set of observed 0.4-2.5 μm spectral-radiance vectors for a material lies in a low-dimensional subspace of the hyperspectral-measurement space. The physical model captures the dependence of the reflected sunlight, reflected skylight, and path-radiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace model, they develop a local maximum-likelihood algorithm for automated material identification that is invariant to illumination, atmospheric conditions, and the scene geometry. The algorithm requires only the spectral reflectance of the target material as input. The authors show that the low dimensionality of material subspaces allows for the robust discrimination of a large number of materials over a wide range of conditions. They demonstrate the invariant algorithm for the automated identification of material samples in HYDICE imagery acquired under different illumination and atmospheric conditions

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:37 ,  Issue: 6 )