Modeling distribution changes for hyperspectral image analysis

Chia-Yun Kuan; Glenn E. Healey

doi:10.1117/1.2802133

1 November 2007 Modeling distribution changes for hyperspectral image analysis

Chia-Yun Kuan, Glenn E. Healey

Author Affiliations +

Optical Engineering, Vol. 46, Issue 11, 117201 (November 2007). https://doi.org/10.1117/1.2802133

Abstract

We use physical considerations to show that an affine transformation can be used to model the effect of environmental changes on hyperspectral image distributions. This allows the generation of a vector of moment invariants that describes an image distribution but does not depend on the environmental conditions. These vectors maintain the invariant property after each image band is spatially filtered which allows the representation to capture spatial properties. We use the distribution invariants and the Fisher discriminant to reduce the size of the representation by selecting optimized spectral bands. We apply the methods developed in this work to the illumination-invariant classification and recognition of regions in airborne images. We also show that the distribution transformation model can be used for change detection in regions viewed under unknown conditions.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Chia-Yun Kuan and Glenn E. Healey "Modeling distribution changes for hyperspectral image analysis," Optical Engineering 46(11), 117201 (1 November 2007). https://doi.org/10.1117/1.2802133

Published: 1 November 2007

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