Invariant recognition in hyperspectral images
Healey, G.
Slater, D.
Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA;
This paper appears in: Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Publication Date: 1999
Volume: 1,
On page(s): -443 Vol. 1
Meeting Date: 06/23/1999 - 06/25/1999
Location: Fort Collins, CO, USA
ISBN: 0-7695-0149-4
References Cited: 6
INSPEC Accession Number: 6338058
Digital Object Identifier: 10.1109/CVPR.1999.786975
Current Version Published: 2002-08-06
Abstract
The spectral radiance measured for a material by an airborne
hyperspectral sensor depends strongly on. The illumination environment
and the atmospheric conditions. This dependence has limited the success
of material identification algorithms that rely exclusively on the
information contained in hyperspectral image data. In this paper we 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
lour-dimensional subspace of the hyperspectral measurement space. The
physical model captures the dependence of 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, we develop a local maximum
likelihood algorithm for automated material identification that is
invariant to illumination, atmospheric conditions, and the scene
geometry. We demonstrate the invariant algorithm for the automated
identification of material samples in HYDICE imagery acquired under
different illumination and atmospheric conditions
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